From d7bb5d5b6b89320df3754d414d5f9b5a9e579593 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Sat, 7 Sep 2024 15:36:38 +0000 Subject: [PATCH] build based on c724446 --- dev/.documenter-siteinfo.json | 2 +- dev/benchmark/{7a93a309.svg => fe1438ac.svg} | 118 +++--- dev/benchmark/index.html | 408 +++++++++---------- dev/contributing/index.html | 2 +- dev/index.html | 2 +- dev/meta/index.html | 4 +- dev/objects.inv | Bin 1780 -> 1798 bytes dev/reference/index.html | 2 +- dev/search_index.js | 2 +- dev/tutorial/index.html | 6 +- 10 files changed, 273 insertions(+), 273 deletions(-) rename dev/benchmark/{7a93a309.svg => fe1438ac.svg} (63%) diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 1c6321e4..39797e15 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-07T12:49:31","documenter_version":"1.7.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-07T15:36:32","documenter_version":"1.7.0"}} \ No newline at end of file diff --git a/dev/benchmark/7a93a309.svg b/dev/benchmark/fe1438ac.svg similarity index 63% rename from dev/benchmark/7a93a309.svg rename to dev/benchmark/fe1438ac.svg index be946b79..2ef08a5e 100644 --- a/dev/benchmark/7a93a309.svg +++ b/dev/benchmark/fe1438ac.svg @@ -1,78 +1,78 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/benchmark/index.html b/dev/benchmark/index.html index 59fd619d..25f20683 100644 --- a/dev/benchmark/index.html +++ b/dev/benchmark/index.html @@ -28,220 +28,220 @@ end
┌────────┬──────────────┬────────┬───────────┬───────────┬─────────┬────────┬────────┬────────┬───────────┬─────────────┐
 │     id │         name │      n │      f(x) │   ‖∇f(x)‖ │     # f │   # ∇f │  # ∇²f │   iter │         t │      status │
 ├────────┼──────────────┼────────┼───────────┼───────────┼─────────┼────────┼────────┼────────┼───────────┼─────────────┤
-│     25 │         NZF1 │     91 │  2.09e+04 │  1.18e-06 │      10 │     10 │      0 │      9 │  6.13e-01 │ first_order │
-│     38 │      arglina │    100 │  5.00e+01 │  7.40e-15 │       6 │      6 │      0 │      5 │  9.75e-02 │ first_order │
-│     39 │      arglinb │    100 │  2.49e+01 │  3.20e-03 │  468470 │ 468296 │      0 │ 468287 │  3.00e+01 │    max_time │
-│     40 │      arglinc │    100 │  5.12e+01 │  3.85e-03 │  264556 │ 264489 │      0 │ 264167 │  2.01e+01 │    not_desc │
-│     41 │      argtrig │    100 │ -9.90e+03 │  7.50e-06 │      38 │     26 │      0 │     25 │  3.44e-03 │ first_order │
-│     42 │      arwhead │    100 │  0.00e+00 │  1.04e-06 │       6 │      6 │      0 │      5 │  3.67e-04 │ first_order │
-│     45 │      bdqrtic │    100 │  1.89e+02 │  4.99e-06 │      13 │     13 │      0 │     12 │  3.95e-03 │ first_order │
+│     25 │         NZF1 │     91 │  2.09e+04 │  1.18e-06 │      10 │     10 │      0 │      9 │  6.05e-01 │ first_order │
+│     38 │      arglina │    100 │  5.00e+01 │  7.40e-15 │       6 │      6 │      0 │      5 │  9.35e-02 │ first_order │
+│     39 │      arglinb │    100 │  2.49e+01 │  3.20e-03 │  469432 │ 469258 │      0 │ 469249 │  3.00e+01 │    max_time │
+│     40 │      arglinc │    100 │  5.12e+01 │  3.85e-03 │  264556 │ 264489 │      0 │ 264167 │  2.02e+01 │    not_desc │
+│     41 │      argtrig │    100 │ -9.90e+03 │  7.50e-06 │      38 │     26 │      0 │     25 │  3.42e-03 │ first_order │
+│     42 │      arwhead │    100 │  0.00e+00 │  1.04e-06 │       6 │      6 │      0 │      5 │  3.76e-04 │ first_order │
+│     45 │      bdqrtic │    100 │  1.89e+02 │  4.99e-06 │      13 │     13 │      0 │     12 │  3.98e-03 │ first_order │
 │     50 │       biggs6 │      6 │ -3.91e+19 │  5.08e+19 │       8 │      8 │      0 │      7 │  1.51e-04 │   unbounded │
-│     55 │      brownal │    100 │ -5.16e-08 │  9.17e-07 │       3 │      3 │      0 │      2 │  1.74e-03 │ first_order │
-│     58 │    broyden3d │    100 │  2.13e-13 │  3.57e-06 │       7 │      7 │      0 │      6 │  9.34e-04 │ first_order │
-│     59 │     broydn7d │    100 │  3.95e+01 │  8.10e-06 │      63 │     22 │      0 │     21 │  3.61e-02 │ first_order │
-│     60 │       brybnd │    100 │  3.08e-13 │  4.90e-06 │       9 │      9 │      0 │      8 │  1.80e-03 │ first_order │
-│     65 │     chainwoo │    100 │  1.11e+02 │  9.98e-06 │     137 │     62 │      0 │     65 │  9.30e-02 │ first_order │
-│     67 │ chnrosnb_mod │    100 │  5.03e-13 │  9.34e-06 │     187 │     95 │      0 │     96 │  4.89e-02 │ first_order │
-│     72 │     clplatea │    100 │ -9.15e-03 │  7.56e-06 │       6 │      6 │      0 │      5 │  4.07e-03 │ first_order │
-│     73 │     clplateb │    100 │ -6.20e-03 │  9.70e-06 │       4 │      4 │      0 │      3 │  3.52e-03 │ first_order │
+│     55 │      brownal │    100 │ -5.16e-08 │  9.17e-07 │       3 │      3 │      0 │      2 │  1.82e-03 │ first_order │
+│     58 │    broyden3d │    100 │  2.13e-13 │  3.57e-06 │       7 │      7 │      0 │      6 │  9.36e-04 │ first_order │
+│     59 │     broydn7d │    100 │  3.95e+01 │  8.10e-06 │      63 │     22 │      0 │     21 │  3.64e-02 │ first_order │
+│     60 │       brybnd │    100 │  3.08e-13 │  4.90e-06 │       9 │      9 │      0 │      8 │  1.90e-03 │ first_order │
+│     65 │     chainwoo │    100 │  1.11e+02 │  9.98e-06 │     137 │     62 │      0 │     65 │  9.25e-02 │ first_order │
+│     67 │ chnrosnb_mod │    100 │  5.03e-13 │  9.34e-06 │     187 │     95 │      0 │     96 │  4.62e-02 │ first_order │
+│     72 │     clplatea │    100 │ -9.15e-03 │  7.56e-06 │       6 │      6 │      0 │      5 │  4.01e-03 │ first_order │
+│     73 │     clplateb │    100 │ -6.20e-03 │  9.70e-06 │       4 │      4 │      0 │      3 │  3.54e-03 │ first_order │
 │     74 │     clplatec │    100 │ -5.11e-03 │  8.28e-06 │       5 │      5 │      0 │      4 │  1.10e-02 │ first_order │
-│     76 │       cosine │    100 │ -9.90e+01 │  9.12e-06 │      10 │     10 │      0 │      9 │  4.87e-04 │ first_order │
-│     77 │     cragglvy │    100 │  3.23e+01 │  7.75e-06 │      16 │     16 │      0 │     15 │  7.15e-03 │ first_order │
-│     78 │    cragglvy2 │    100 │  2.52e+01 │  6.43e-06 │      18 │     18 │      0 │     17 │  8.61e-03 │ first_order │
-│     79 │        curly │    100 │ -1.00e+04 │  8.29e-06 │      18 │     15 │      0 │     14 │  2.33e-01 │ first_order │
-│     80 │      curly10 │    100 │ -1.00e+04 │  8.29e-06 │      18 │     15 │      0 │     14 │  2.16e-01 │ first_order │
-│     81 │      curly20 │    100 │ -1.00e+04 │  9.23e-06 │      18 │     14 │      0 │     13 │  5.51e-01 │ first_order │
-│     82 │      curly30 │    100 │ -1.00e+04 │  8.43e-06 │      28 │     14 │      0 │     13 │  9.92e-01 │ first_order │
-│     84 │     dixmaane │     99 │  1.00e+00 │  8.12e-06 │      39 │     16 │      0 │     15 │  4.45e-03 │ first_order │
-│     85 │     dixmaanf │     99 │  1.00e+00 │  9.59e-06 │      11 │     11 │      0 │     10 │  2.89e-03 │ first_order │
-│     86 │     dixmaang │     99 │  1.00e+00 │  9.88e-06 │      43 │     15 │      0 │     16 │  4.67e-03 │ first_order │
-│     87 │     dixmaanh │     99 │  1.00e+00 │  7.74e-06 │      37 │     15 │      0 │     16 │  4.16e-03 │ first_order │
-│     88 │     dixmaani │     99 │  1.00e+00 │  8.68e-06 │      15 │     13 │      0 │     12 │  1.15e-02 │ first_order │
-│     89 │     dixmaanj │     99 │  1.00e+00 │  9.47e-06 │      39 │     17 │      0 │     18 │  1.42e-02 │ first_order │
+│     76 │       cosine │    100 │ -9.90e+01 │  9.12e-06 │      10 │     10 │      0 │      9 │  4.89e-04 │ first_order │
+│     77 │     cragglvy │    100 │  3.23e+01 │  7.75e-06 │      16 │     16 │      0 │     15 │  7.11e-03 │ first_order │
+│     78 │    cragglvy2 │    100 │  2.52e+01 │  6.43e-06 │      18 │     18 │      0 │     17 │  8.71e-03 │ first_order │
+│     79 │        curly │    100 │ -1.00e+04 │  8.29e-06 │      18 │     15 │      0 │     14 │  2.21e-01 │ first_order │
+│     80 │      curly10 │    100 │ -1.00e+04 │  8.29e-06 │      18 │     15 │      0 │     14 │  2.21e-01 │ first_order │
+│     81 │      curly20 │    100 │ -1.00e+04 │  9.23e-06 │      18 │     14 │      0 │     13 │  5.58e-01 │ first_order │
+│     82 │      curly30 │    100 │ -1.00e+04 │  8.43e-06 │      28 │     14 │      0 │     13 │  1.12e+00 │ first_order │
+│     84 │     dixmaane │     99 │  1.00e+00 │  8.12e-06 │      39 │     16 │      0 │     15 │  4.48e-03 │ first_order │
+│     85 │     dixmaanf │     99 │  1.00e+00 │  9.59e-06 │      11 │     11 │      0 │     10 │  2.95e-03 │ first_order │
+│     86 │     dixmaang │     99 │  1.00e+00 │  9.88e-06 │      43 │     15 │      0 │     16 │  4.76e-03 │ first_order │
+│     87 │     dixmaanh │     99 │  1.00e+00 │  7.74e-06 │      37 │     15 │      0 │     16 │  4.23e-03 │ first_order │
+│     88 │     dixmaani │     99 │  1.00e+00 │  8.68e-06 │      15 │     13 │      0 │     12 │  1.16e-02 │ first_order │
+│     89 │     dixmaanj │     99 │  1.00e+00 │  9.47e-06 │      39 │     17 │      0 │     18 │  1.44e-02 │ first_order │
 │     90 │     dixmaank │     99 │  1.00e+00 │  1.00e-05 │      38 │     16 │      0 │     16 │  1.11e-02 │    not_desc │
 │     91 │     dixmaanl │     99 │  1.00e+00 │  9.94e-06 │      56 │     18 │      0 │     19 │  1.29e-02 │ first_order │
-│     92 │     dixmaanm │     99 │  1.00e+00 │  8.95e-06 │      10 │     10 │      0 │      9 │  9.58e-03 │ first_order │
-│     93 │     dixmaann │     99 │  1.00e+00 │  9.79e-06 │      43 │     20 │      0 │     21 │  2.03e-02 │ first_order │
-│     94 │     dixmaano │     99 │  1.00e+00 │  6.61e-06 │      39 │     17 │      0 │     18 │  1.54e-02 │ first_order │
-│     95 │     dixmaanp │     99 │  1.00e+00 │  8.90e-06 │      39 │     17 │      0 │     18 │  1.45e-02 │ first_order │
-│     96 │     dixon3dq │    100 │  3.83e-10 │  9.84e-06 │      10 │     10 │      0 │      9 │  1.68e-04 │ first_order │
-│     97 │      dqdrtic │    100 │  1.37e-14 │  9.42e-07 │       6 │      6 │      0 │      5 │  4.20e-05 │ first_order │
-│     98 │       dqrtic │    100 │  8.30e-08 │  6.24e-06 │      25 │     25 │      0 │     24 │  8.26e-04 │ first_order │
-│    100 │      edensch │    100 │  6.03e+02 │  4.70e-06 │       9 │      9 │      0 │      8 │  1.22e-03 │ first_order │
-│    101 │          eg2 │    100 │ -9.89e+01 │  1.42e-09 │       4 │      4 │      0 │      3 │  1.47e-04 │ first_order │
-│    103 │      engval1 │    100 │  1.09e+02 │  4.98e-06 │      10 │     10 │      0 │      9 │  8.99e-04 │ first_order │
-│    104 │         enso │      9 │  3.94e+02 │  9.19e-06 │      39 │     17 │      0 │     18 │  1.28e-02 │ first_order │
-│    105 │ errinros_mod │    100 │  3.88e+01 │  8.68e-06 │     121 │     78 │      0 │     79 │  2.20e-02 │ first_order │
-│    106 │     extrosnb │    100 │  1.99e-06 │  9.61e-06 │     924 │    251 │      0 │    250 │  5.53e-02 │ first_order │
-│    107 │     fletcbv2 │    100 │ -5.14e-01 │  8.49e-06 │       5 │      5 │      0 │      4 │  2.75e-03 │ first_order │
-│    108 │ fletcbv3_mod │    100 │ -2.04e+00 │  3.49e-06 │     173 │     60 │      0 │     61 │  8.96e-03 │ first_order │
+│     92 │     dixmaanm │     99 │  1.00e+00 │  8.95e-06 │      10 │     10 │      0 │      9 │  9.62e-03 │ first_order │
+│     93 │     dixmaann │     99 │  1.00e+00 │  9.79e-06 │      43 │     20 │      0 │     21 │  2.06e-02 │ first_order │
+│     94 │     dixmaano │     99 │  1.00e+00 │  6.61e-06 │      39 │     17 │      0 │     18 │  1.56e-02 │ first_order │
+│     95 │     dixmaanp │     99 │  1.00e+00 │  8.90e-06 │      39 │     17 │      0 │     18 │  1.48e-02 │ first_order │
+│     96 │     dixon3dq │    100 │  3.83e-10 │  9.84e-06 │      10 │     10 │      0 │      9 │  1.75e-04 │ first_order │
+│     97 │      dqdrtic │    100 │  1.37e-14 │  9.42e-07 │       6 │      6 │      0 │      5 │  4.29e-05 │ first_order │
+│     98 │       dqrtic │    100 │  8.30e-08 │  6.24e-06 │      25 │     25 │      0 │     24 │  8.29e-04 │ first_order │
+│    100 │      edensch │    100 │  6.03e+02 │  4.70e-06 │       9 │      9 │      0 │      8 │  1.23e-03 │ first_order │
+│    101 │          eg2 │    100 │ -9.89e+01 │  1.42e-09 │       4 │      4 │      0 │      3 │  1.53e-04 │ first_order │
+│    103 │      engval1 │    100 │  1.09e+02 │  4.98e-06 │      10 │     10 │      0 │      9 │  9.03e-04 │ first_order │
+│    104 │         enso │      9 │  3.94e+02 │  9.19e-06 │      39 │     17 │      0 │     18 │  1.25e-02 │ first_order │
+│    105 │ errinros_mod │    100 │  3.88e+01 │  8.68e-06 │     121 │     78 │      0 │     79 │  2.18e-02 │ first_order │
+│    106 │     extrosnb │    100 │  1.99e-06 │  9.61e-06 │     924 │    251 │      0 │    250 │  5.48e-02 │ first_order │
+│    107 │     fletcbv2 │    100 │ -5.14e-01 │  8.49e-06 │       5 │      5 │      0 │      4 │  2.68e-03 │ first_order │
+│    108 │ fletcbv3_mod │    100 │ -2.04e+00 │  3.49e-06 │     173 │     60 │      0 │     61 │  9.18e-03 │ first_order │
 │    109 │     fletchcr │    100 │  6.61e-12 │  8.26e-06 │      99 │     77 │      0 │     78 │  1.28e-02 │ first_order │
-│    110 │     fminsrf2 │    100 │  1.00e+02 │  7.59e-06 │     190 │     45 │      0 │     46 │  1.67e-02 │ first_order │
-│    111 │     freuroth │    100 │  5.94e+03 │  2.30e-06 │      48 │     16 │      0 │     17 │  4.21e-03 │ first_order │
-│    112 │       gauss1 │      8 │  6.58e+02 │  9.67e-06 │      12 │     12 │      0 │     11 │  1.18e-02 │ first_order │
-│    113 │       gauss2 │      8 │  6.24e+02 │  7.74e-06 │      14 │     14 │      0 │     13 │  1.41e-02 │ first_order │
-│    114 │       gauss3 │      8 │  6.22e+02 │  3.26e-05 │     349 │    607 │      0 │    310 │  1.83e-02 │    not_desc │
-│    116 │     genhumps │    100 │  1.73e-14 │  8.32e-08 │   10333 │   3453 │      0 │   3464 │  9.41e-01 │ first_order │
-│    117 │      genrose │    100 │  1.00e+00 │  6.62e-06 │     159 │     60 │      0 │     61 │  1.38e-02 │ first_order │
+│    110 │     fminsrf2 │    100 │  1.00e+02 │  7.59e-06 │     190 │     45 │      0 │     46 │  1.66e-02 │ first_order │
+│    111 │     freuroth │    100 │  5.94e+03 │  2.30e-06 │      48 │     16 │      0 │     17 │  4.25e-03 │ first_order │
+│    112 │       gauss1 │      8 │  6.58e+02 │  9.67e-06 │      12 │     12 │      0 │     11 │  1.21e-02 │ first_order │
+│    113 │       gauss2 │      8 │  6.24e+02 │  7.74e-06 │      14 │     14 │      0 │     13 │  1.42e-02 │ first_order │
+│    114 │       gauss3 │      8 │  6.22e+02 │  3.26e-05 │     349 │    607 │      0 │    310 │  1.86e-02 │    not_desc │
+│    116 │     genhumps │    100 │  1.73e-14 │  8.32e-08 │   10333 │   3453 │      0 │   3464 │  9.45e-01 │ first_order │
+│    117 │      genrose │    100 │  1.00e+00 │  6.62e-06 │     159 │     60 │      0 │     61 │  1.39e-02 │ first_order │
 │    118 │ genrose_nash │    100 │  1.00e+00 │  4.93e-06 │     195 │     66 │      0 │     67 │  1.53e-02 │ first_order │
-│    120 │        hahn1 │      7 │  7.66e-01 │  3.65e-06 │     234 │    118 │      0 │    127 │  9.23e-02 │ first_order │
-│    286 │    indef_mod │    100 │ -9.97e+03 │  8.93e-06 │     239 │    213 │      0 │    214 │  2.41e-02 │ first_order │
-│    287 │     integreq │    100 │  4.47e-13 │  9.84e-07 │       4 │      4 │      0 │      3 │  1.53e-02 │ first_order │
-│    289 │       kirby2 │      5 │  1.95e+00 │  1.16e-05 │     344 │    622 │      0 │    324 │  5.78e-03 │    not_desc │
-│    291 │     lanczos1 │      6 │  1.05e-06 │  6.96e-06 │     118 │     37 │      0 │     38 │  2.68e-03 │ first_order │
-│    292 │     lanczos2 │      6 │  9.83e-07 │  9.41e-06 │     121 │     35 │      0 │     36 │  2.60e-03 │ first_order │
-│    293 │     lanczos3 │      6 │  1.16e-06 │  7.97e-06 │     120 │     38 │      0 │     39 │  3.08e-03 │ first_order │
-│    294 │      liarwhd │    100 │  1.41e-13 │  7.82e-06 │      11 │     11 │      0 │     10 │  1.15e-03 │ first_order │
-│    302 │        mgh17 │      5 │  5.52e-02 │  1.56e-06 │      31 │      8 │      0 │      9 │  2.81e-04 │ first_order │
-│    307 │       morebv │    100 │  1.85e-06 │  8.80e-06 │      14 │     14 │      0 │     13 │  5.70e-02 │ first_order │
-│    309 │        ncb20 │    100 │  1.64e+02 │  8.43e-06 │     128 │     27 │      0 │     26 │  8.34e-02 │ first_order │
-│    310 │       ncb20b │    100 │  1.97e+02 │  8.00e-06 │      38 │     15 │      0 │     16 │  1.81e-01 │ first_order │
-│    312 │     noncvxu2 │    100 │  2.34e+02 │  9.72e-06 │     138 │     36 │      0 │     37 │  2.96e-02 │ first_order │
-│    313 │     noncvxun │    100 │  2.35e+02 │  6.97e-06 │     131 │     35 │      0 │     36 │  1.82e-02 │ first_order │
-│    314 │       nondia │    100 │  1.05e-13 │  1.28e-07 │      12 │      9 │      0 │      8 │  3.35e-04 │ first_order │
-│    315 │     nondquar │    100 │  3.51e-06 │  9.63e-06 │      62 │     28 │      0 │     27 │  7.06e-03 │ first_order │
-│    316 │     osborne1 │      5 │  2.82e-05 │  9.44e-06 │     171 │     53 │      0 │     55 │  4.00e-03 │ first_order │
-│    317 │     osborne2 │     11 │  2.01e-02 │  5.36e-06 │      26 │     19 │      0 │     18 │  1.07e-02 │ first_order │
-│    318 │     palmer1c │      8 │  4.88e-02 │  6.40e-06 │      15 │     10 │      0 │      9 │  6.32e-05 │ first_order │
-│    319 │     palmer1d │      7 │  3.26e-01 │  2.24e-08 │       7 │      7 │      0 │      6 │  5.10e-05 │ first_order │
-│    320 │     palmer2c │      8 │  7.21e-03 │  4.85e-06 │       7 │      7 │      0 │      6 │  6.20e-05 │ first_order │
-│    321 │     palmer3c │      8 │  9.77e-03 │  7.91e-06 │       7 │      7 │      0 │      6 │  4.82e-05 │ first_order │
-│    322 │     palmer4c │      8 │  2.85e-02 │  1.10e-08 │      11 │     11 │      0 │     10 │  6.20e-05 │ first_order │
-│    323 │     palmer5c │      6 │  1.06e+00 │  9.22e-07 │       7 │      7 │      0 │      6 │  4.10e-05 │ first_order │
-│    325 │     palmer6c │      8 │  8.19e-03 │  4.90e-09 │       9 │      9 │      0 │      8 │  5.41e-05 │ first_order │
-│    326 │     palmer7c │      8 │  3.01e-01 │  4.89e-06 │      16 │     15 │      0 │     14 │  7.49e-05 │ first_order │
-│    327 │     palmer8c │      8 │  7.99e-02 │  7.53e-06 │      11 │     11 │      0 │     10 │  5.98e-05 │ first_order │
-│    328 │     penalty1 │    100 │  4.51e-04 │  9.69e-06 │      54 │     32 │      0 │     31 │  1.72e-03 │ first_order │
-│    329 │     penalty2 │    100 │  9.71e+04 │  8.67e-06 │      19 │     19 │      0 │     18 │  6.44e-03 │ first_order │
-│    330 │     penalty3 │    100 │  4.30e+07 │  6.81e+12 │ 1071420 │  97438 │      0 │  97446 │  3.00e+01 │    max_time │
-│    336 │     powellsg │    100 │  6.13e-08 │  8.57e-06 │      18 │     18 │      0 │     17 │  1.54e-03 │ first_order │
-│    337 │        power │    100 │  1.50e-08 │  7.99e-06 │      24 │     24 │      0 │     23 │  1.47e-03 │ first_order │
-│    338 │       quartc │    100 │  8.30e-08 │  6.24e-06 │      25 │     25 │      0 │     24 │  8.22e-04 │ first_order │
-│    344 │      sbrybnd │    100 │  1.34e+00 │  2.29e-05 │    1742 │   1698 │      0 │   1701 │  3.00e+01 │    max_time │
-│    345 │     schmvett │    100 │ -2.94e+02 │  8.24e-06 │      14 │     12 │      0 │     11 │  3.75e-03 │ first_order │
-│    346 │      scosine │    100 │ -9.90e+01 │  9.19e-06 │     391 │    298 │      0 │    307 │  7.40e-01 │ first_order │
-│    347 │      sinquad │    100 │  6.11e-07 │  8.40e-06 │      65 │     33 │      0 │     34 │  4.21e-03 │ first_order │
-│    348 │     sparsine │    100 │  2.11e-12 │  8.42e-06 │      81 │     33 │      0 │     34 │  3.47e-02 │ first_order │
-│    349 │     sparsqur │    100 │  1.77e-08 │  6.42e-06 │      17 │     17 │      0 │     16 │  3.01e-03 │ first_order │
-│    350 │     spmsrtls │    100 │  8.17e-11 │  9.01e-06 │      13 │     13 │      0 │     12 │  3.38e-03 │ first_order │
-│    351 │     srosenbr │    100 │  3.06e-15 │  2.47e-06 │      60 │     26 │      0 │     27 │  1.52e-03 │ first_order │
-│    361 │      thurber │      7 │  2.82e+03 │  6.94e-06 │      59 │     42 │      0 │     41 │  4.21e-03 │ first_order │
-│    362 │     tointgss │    100 │  9.71e+00 │  8.20e-06 │      35 │     13 │      0 │     14 │  2.67e-03 │ first_order │
-│    363 │     tquartic │    100 │  4.90e-12 │  3.14e-07 │      15 │     13 │      0 │     12 │  6.84e-04 │ first_order │
-│    368 │       tridia │    100 │  1.36e-13 │  8.96e-06 │       8 │      8 │      0 │      7 │  1.32e-04 │ first_order │
-│    369 │       vardim │    100 │  3.73e-09 │  0.00e+00 │      26 │     26 │      0 │     25 │  1.96e-02 │ first_order │
-│    370 │     vibrbeam │      8 │  7.82e-02 │  3.74e-06 │      33 │     32 │      0 │     31 │  2.84e-03 │ first_order │
-│    371 │       watson │     31 │ -5.94e+64 │  6.31e+65 │       2 │      2 │      0 │      1 │  5.72e-03 │   unbounded │
-│    372 │        woods │    100 │  3.92e-17 │  2.81e-07 │      82 │     40 │      0 │     41 │  7.31e-03 │ first_order │
+│    120 │        hahn1 │      7 │  7.66e-01 │  3.65e-06 │     234 │    118 │      0 │    127 │  9.58e-02 │ first_order │
+│    287 │    indef_mod │    100 │ -9.97e+03 │  8.93e-06 │     239 │    213 │      0 │    214 │  2.44e-02 │ first_order │
+│    288 │     integreq │    100 │  4.47e-13 │  9.84e-07 │       4 │      4 │      0 │      3 │  1.52e-02 │ first_order │
+│    290 │       kirby2 │      5 │  1.95e+00 │  1.16e-05 │     344 │    622 │      0 │    324 │  5.80e-03 │    not_desc │
+│    292 │     lanczos1 │      6 │  1.05e-06 │  6.96e-06 │     118 │     37 │      0 │     38 │  2.68e-03 │ first_order │
+│    293 │     lanczos2 │      6 │  9.83e-07 │  9.41e-06 │     121 │     35 │      0 │     36 │  2.61e-03 │ first_order │
+│    294 │     lanczos3 │      6 │  1.16e-06 │  7.97e-06 │     120 │     38 │      0 │     39 │  2.77e-03 │ first_order │
+│    295 │      liarwhd │    100 │  1.41e-13 │  7.82e-06 │      11 │     11 │      0 │     10 │  1.17e-03 │ first_order │
+│    303 │        mgh17 │      5 │  5.52e-02 │  1.56e-06 │      31 │      8 │      0 │      9 │  2.86e-04 │ first_order │
+│    308 │       morebv │    100 │  1.85e-06 │  8.80e-06 │      14 │     14 │      0 │     13 │  5.58e-02 │ first_order │
+│    310 │        ncb20 │    100 │  1.64e+02 │  8.43e-06 │     128 │     27 │      0 │     26 │  8.53e-02 │ first_order │
+│    311 │       ncb20b │    100 │  1.97e+02 │  8.00e-06 │      38 │     15 │      0 │     16 │  1.86e-01 │ first_order │
+│    313 │     noncvxu2 │    100 │  2.34e+02 │  9.72e-06 │     138 │     36 │      0 │     37 │  3.01e-02 │ first_order │
+│    314 │     noncvxun │    100 │  2.35e+02 │  6.97e-06 │     131 │     35 │      0 │     36 │  1.85e-02 │ first_order │
+│    315 │       nondia │    100 │  1.05e-13 │  1.28e-07 │      12 │      9 │      0 │      8 │  3.56e-04 │ first_order │
+│    316 │     nondquar │    100 │  3.51e-06 │  9.63e-06 │      62 │     28 │      0 │     27 │  7.07e-03 │ first_order │
+│    317 │     osborne1 │      5 │  2.82e-05 │  9.44e-06 │     171 │     53 │      0 │     55 │  4.06e-03 │ first_order │
+│    318 │     osborne2 │     11 │  2.01e-02 │  5.36e-06 │      26 │     19 │      0 │     18 │  1.07e-02 │ first_order │
+│    319 │     palmer1c │      8 │  4.88e-02 │  6.40e-06 │      15 │     10 │      0 │      9 │  6.60e-05 │ first_order │
+│    320 │     palmer1d │      7 │  3.26e-01 │  2.24e-08 │       7 │      7 │      0 │      6 │  4.48e-05 │ first_order │
+│    321 │     palmer2c │      8 │  7.21e-03 │  4.85e-06 │       7 │      7 │      0 │      6 │  4.70e-05 │ first_order │
+│    322 │     palmer3c │      8 │  9.77e-03 │  7.91e-06 │       7 │      7 │      0 │      6 │  4.89e-05 │ first_order │
+│    323 │     palmer4c │      8 │  2.85e-02 │  1.10e-08 │      11 │     11 │      0 │     10 │  6.20e-05 │ first_order │
+│    324 │     palmer5c │      6 │  1.06e+00 │  9.22e-07 │       7 │      7 │      0 │      6 │  4.01e-05 │ first_order │
+│    326 │     palmer6c │      8 │  8.19e-03 │  4.90e-09 │       9 │      9 │      0 │      8 │  5.72e-05 │ first_order │
+│    327 │     palmer7c │      8 │  3.01e-01 │  4.89e-06 │      16 │     15 │      0 │     14 │  7.49e-05 │ first_order │
+│    328 │     palmer8c │      8 │  7.99e-02 │  7.53e-06 │      11 │     11 │      0 │     10 │  6.10e-05 │ first_order │
+│    329 │     penalty1 │    100 │  4.51e-04 │  9.69e-06 │      54 │     32 │      0 │     31 │  1.74e-03 │ first_order │
+│    330 │     penalty2 │    100 │  9.71e+04 │  8.67e-06 │      19 │     19 │      0 │     18 │  6.47e-03 │ first_order │
+│    331 │     penalty3 │    100 │  4.30e+07 │  6.81e+12 │ 1051631 │  95639 │      0 │  95647 │  3.00e+01 │    max_time │
+│    337 │     powellsg │    100 │  6.13e-08 │  8.57e-06 │      18 │     18 │      0 │     17 │  1.53e-03 │ first_order │
+│    338 │        power │    100 │  1.50e-08 │  7.99e-06 │      24 │     24 │      0 │     23 │  1.48e-03 │ first_order │
+│    339 │       quartc │    100 │  8.30e-08 │  6.24e-06 │      25 │     25 │      0 │     24 │  8.18e-04 │ first_order │
+│    345 │      sbrybnd │    100 │  1.34e+00 │  9.27e-06 │    1765 │   1721 │      0 │   1724 │  2.92e+01 │ first_order │
+│    346 │     schmvett │    100 │ -2.94e+02 │  8.24e-06 │      14 │     12 │      0 │     11 │  3.72e-03 │ first_order │
+│    347 │      scosine │    100 │ -9.90e+01 │  9.19e-06 │     391 │    298 │      0 │    307 │  7.34e-01 │ first_order │
+│    348 │      sinquad │    100 │  6.11e-07 │  8.40e-06 │      65 │     33 │      0 │     34 │  4.25e-03 │ first_order │
+│    349 │     sparsine │    100 │  2.11e-12 │  8.42e-06 │      81 │     33 │      0 │     34 │  3.48e-02 │ first_order │
+│    350 │     sparsqur │    100 │  1.77e-08 │  6.42e-06 │      17 │     17 │      0 │     16 │  3.04e-03 │ first_order │
+│    351 │     spmsrtls │    100 │  8.17e-11 │  9.01e-06 │      13 │     13 │      0 │     12 │  3.33e-03 │ first_order │
+│    352 │     srosenbr │    100 │  3.06e-15 │  2.47e-06 │      60 │     26 │      0 │     27 │  1.51e-03 │ first_order │
+│    362 │      thurber │      7 │  2.82e+03 │  6.94e-06 │      59 │     42 │      0 │     41 │  4.20e-03 │ first_order │
+│    363 │     tointgss │    100 │  9.71e+00 │  8.20e-06 │      35 │     13 │      0 │     14 │  2.69e-03 │ first_order │
+│    364 │     tquartic │    100 │  4.90e-12 │  3.14e-07 │      15 │     13 │      0 │     12 │  6.89e-04 │ first_order │
+│    369 │       tridia │    100 │  1.36e-13 │  8.96e-06 │       8 │      8 │      0 │      7 │  1.36e-04 │ first_order │
+│    370 │       vardim │    100 │  3.73e-09 │  0.00e+00 │      26 │     26 │      0 │     25 │  1.99e-02 │ first_order │
+│    371 │     vibrbeam │      8 │  7.82e-02 │  3.74e-06 │      33 │     32 │      0 │     31 │  2.77e-03 │ first_order │
+│    372 │       watson │     31 │ -5.94e+64 │  6.31e+65 │       2 │      2 │      0 │      1 │  5.62e-03 │   unbounded │
+│    373 │        woods │    100 │  3.92e-17 │  2.81e-07 │      82 │     40 │      0 │     41 │  7.41e-03 │ first_order │
 └────────┴──────────────┴────────┴───────────┴───────────┴─────────┴────────┴────────┴────────┴───────────┴─────────────┘
 ┌────────┬──────────────┬────────┬───────────┬───────────┬───────────┬─────────┬────────┬─────────┬───────────┬─────────────┐
 │     id │         name │      n │      f(x) │   ‖∇f(x)‖ │       # f │    # ∇f │  # ∇²f │    iter │         t │      status │
 ├────────┼──────────────┼────────┼───────────┼───────────┼───────────┼─────────┼────────┼─────────┼───────────┼─────────────┤
-│     25 │         NZF1 │     91 │  2.09e+04 │  9.54e-06 │       266 │     260 │      0 │     240 │  5.29e-03 │ first_order │
-│     38 │      arglina │    100 │  5.00e+01 │  2.92e-14 │         2 │       2 │      0 │       1 │  1.02e-04 │ first_order │
-│     39 │      arglinb │    100 │  2.48e+01 │  3.99e-04 │   2443402 │  326643 │      0 │  163325 │  3.00e+01 │    max_time │
-│     40 │      arglinc │    100 │  5.11e+01 │  6.35e-04 │   1919517 │  639861 │      0 │  319931 │  3.00e+01 │    max_time │
-│     41 │      argtrig │    100 │ -9.90e+03 │  7.54e-06 │       116 │     114 │      0 │     103 │  3.70e-03 │ first_order │
-│     42 │      arwhead │    100 │  0.00e+00 │  4.34e-06 │        20 │      14 │      0 │      12 │  2.85e-04 │ first_order │
-│     45 │      bdqrtic │    100 │  1.89e+02 │  9.69e-06 │       114 │     102 │      0 │      89 │  3.04e-03 │ first_order │
-│     50 │       biggs6 │      6 │      -Inf │       Inf │         8 │       8 │      0 │       2 │  4.89e-05 │   unbounded │
-│     55 │      brownal │    100 │ -1.97e-08 │  1.58e-06 │        17 │       8 │      0 │       6 │  2.40e-03 │ first_order │
-│     58 │    broyden3d │    100 │  3.56e-01 │  8.29e-06 │        53 │      51 │      0 │      44 │  9.65e-04 │ first_order │
-│     59 │     broydn7d │    100 │  3.60e+01 │  8.88e-06 │       373 │     371 │      0 │     357 │  1.97e-02 │ first_order │
-│     60 │       brybnd │    100 │  1.54e+00 │  5.36e-06 │        25 │      21 │      0 │      19 │  9.16e-04 │ first_order │
-│     65 │     chainwoo │    100 │  1.00e+00 │  9.22e-06 │       816 │     802 │      0 │     749 │  2.58e-02 │ first_order │
-│     67 │ chnrosnb_mod │    100 │  2.51e-11 │  9.29e-06 │       689 │     683 │      0 │     649 │  1.25e-02 │ first_order │
-│     72 │     clplatea │    100 │ -9.15e-03 │  9.75e-06 │        74 │      74 │      0 │      70 │  2.62e-03 │ first_order │
-│     73 │     clplateb │    100 │ -6.20e-03 │  5.93e-06 │        58 │      58 │      0 │      57 │  2.02e-03 │ first_order │
-│     74 │     clplatec │    100 │ -5.11e-03 │  8.28e-06 │       461 │     456 │      0 │     436 │  1.45e-02 │ first_order │
-│     76 │       cosine │    100 │ -9.90e+01 │  1.00e-06 │        15 │      15 │      0 │      11 │  2.38e-04 │ first_order │
-│     77 │     cragglvy │    100 │  3.23e+01 │  9.36e-06 │        93 │      84 │      0 │      81 │  3.89e-03 │ first_order │
-│     78 │    cragglvy2 │    100 │  2.52e+01 │  8.92e-06 │       121 │     112 │      0 │     105 │  4.95e-03 │ first_order │
-│     79 │        curly │    100 │ -1.00e+04 │  9.36e-06 │      1275 │    1266 │      0 │    1226 │  3.06e-01 │ first_order │
-│     80 │      curly10 │    100 │ -1.00e+04 │  9.36e-06 │      1275 │    1266 │      0 │    1226 │  2.89e-01 │ first_order │
-│     81 │      curly20 │    100 │ -1.00e+04 │  9.96e-06 │      1311 │    1305 │      0 │    1238 │  9.24e-01 │ first_order │
-│     82 │      curly30 │    100 │ -1.00e+04 │  9.57e-06 │      1573 │    1566 │      0 │    1475 │  2.12e+00 │ first_order │
-│     84 │     dixmaane │     99 │  1.00e+00 │  7.83e-06 │        62 │      61 │      0 │      58 │  1.36e-03 │ first_order │
-│     85 │     dixmaanf │     99 │  1.00e+00 │  6.94e-06 │        57 │      55 │      0 │      49 │  1.42e-03 │ first_order │
-│     86 │     dixmaang │     99 │  1.00e+00 │  9.82e-06 │        59 │      56 │      0 │      53 │  1.39e-03 │ first_order │
-│     87 │     dixmaanh │     99 │  1.00e+00 │  7.37e-06 │        66 │      63 │      0 │      59 │  1.57e-03 │ first_order │
-│     88 │     dixmaani │     99 │  1.00e+00 │  8.73e-06 │       240 │     239 │      0 │     232 │  5.35e-03 │ first_order │
-│     89 │     dixmaanj │     99 │  1.00e+00 │  9.19e-06 │       261 │     259 │      0 │     250 │  6.36e-03 │ first_order │
-│     90 │     dixmaank │     99 │  1.00e+00 │  9.28e-06 │       209 │     207 │      0 │     197 │  5.23e-03 │ first_order │
-│     91 │     dixmaanl │     99 │  1.00e+00 │  7.52e-06 │       181 │     178 │      0 │     172 │  4.40e-03 │ first_order │
-│     92 │     dixmaanm │     99 │  1.00e+00 │  9.42e-06 │       365 │     364 │      0 │     353 │  8.06e-03 │ first_order │
-│     93 │     dixmaann │     99 │  1.00e+00 │  9.67e-06 │       324 │     323 │      0 │     313 │  8.69e-03 │ first_order │
-│     94 │     dixmaano │     99 │  1.00e+00 │  9.69e-06 │       225 │     223 │      0 │     217 │  5.46e-03 │ first_order │
-│     95 │     dixmaanp │     99 │  1.00e+00 │  7.83e-06 │       307 │     304 │      0 │     295 │  7.63e-03 │ first_order │
-│     96 │     dixon3dq │    100 │  1.79e-08 │  8.88e-06 │       468 │     468 │      0 │     457 │  2.02e-03 │ first_order │
+│     25 │         NZF1 │     91 │  2.09e+04 │  9.54e-06 │       266 │     260 │      0 │     240 │  5.43e-03 │ first_order │
+│     38 │      arglina │    100 │  5.00e+01 │  2.92e-14 │         2 │       2 │      0 │       1 │  8.89e-05 │ first_order │
+│     39 │      arglinb │    100 │  2.48e+01 │  3.99e-04 │   2406607 │  321737 │      0 │  160872 │  3.00e+01 │    max_time │
+│     40 │      arglinc │    100 │  5.11e+01 │  6.35e-04 │   1932033 │  644033 │      0 │  322017 │  3.00e+01 │    max_time │
+│     41 │      argtrig │    100 │ -9.90e+03 │  7.54e-06 │       116 │     114 │      0 │     103 │  3.79e-03 │ first_order │
+│     42 │      arwhead │    100 │  0.00e+00 │  4.34e-06 │        20 │      14 │      0 │      12 │  2.97e-04 │ first_order │
+│     45 │      bdqrtic │    100 │  1.89e+02 │  9.69e-06 │       114 │     102 │      0 │      89 │  3.11e-03 │ first_order │
+│     50 │       biggs6 │      6 │      -Inf │       Inf │         8 │       8 │      0 │       2 │  4.82e-05 │   unbounded │
+│     55 │      brownal │    100 │ -1.97e-08 │  1.58e-06 │        17 │       8 │      0 │       6 │  2.42e-03 │ first_order │
+│     58 │    broyden3d │    100 │  3.56e-01 │  8.29e-06 │        53 │      51 │      0 │      44 │  9.61e-04 │ first_order │
+│     59 │     broydn7d │    100 │  3.60e+01 │  8.88e-06 │       373 │     371 │      0 │     357 │  1.94e-02 │ first_order │
+│     60 │       brybnd │    100 │  1.54e+00 │  5.36e-06 │        25 │      21 │      0 │      19 │  9.46e-04 │ first_order │
+│     65 │     chainwoo │    100 │  1.00e+00 │  9.22e-06 │       816 │     802 │      0 │     749 │  2.64e-02 │ first_order │
+│     67 │ chnrosnb_mod │    100 │  2.51e-11 │  9.29e-06 │       689 │     683 │      0 │     649 │  1.27e-02 │ first_order │
+│     72 │     clplatea │    100 │ -9.15e-03 │  9.75e-06 │        74 │      74 │      0 │      70 │  2.76e-03 │ first_order │
+│     73 │     clplateb │    100 │ -6.20e-03 │  5.93e-06 │        58 │      58 │      0 │      57 │  2.13e-03 │ first_order │
+│     74 │     clplatec │    100 │ -5.11e-03 │  8.28e-06 │       461 │     456 │      0 │     436 │  1.51e-02 │ first_order │
+│     76 │       cosine │    100 │ -9.90e+01 │  1.00e-06 │        15 │      15 │      0 │      11 │  2.42e-04 │ first_order │
+│     77 │     cragglvy │    100 │  3.23e+01 │  9.36e-06 │        93 │      84 │      0 │      81 │  3.93e-03 │ first_order │
+│     78 │    cragglvy2 │    100 │  2.52e+01 │  8.92e-06 │       121 │     112 │      0 │     105 │  5.02e-03 │ first_order │
+│     79 │        curly │    100 │ -1.00e+04 │  9.36e-06 │      1275 │    1266 │      0 │    1226 │  3.02e-01 │ first_order │
+│     80 │      curly10 │    100 │ -1.00e+04 │  9.36e-06 │      1275 │    1266 │      0 │    1226 │  3.00e-01 │ first_order │
+│     81 │      curly20 │    100 │ -1.00e+04 │  9.96e-06 │      1311 │    1305 │      0 │    1238 │  9.43e-01 │ first_order │
+│     82 │      curly30 │    100 │ -1.00e+04 │  9.57e-06 │      1573 │    1566 │      0 │    1475 │  2.15e+00 │ first_order │
+│     84 │     dixmaane │     99 │  1.00e+00 │  7.83e-06 │        62 │      61 │      0 │      58 │  1.38e-03 │ first_order │
+│     85 │     dixmaanf │     99 │  1.00e+00 │  6.94e-06 │        57 │      55 │      0 │      49 │  1.46e-03 │ first_order │
+│     86 │     dixmaang │     99 │  1.00e+00 │  9.82e-06 │        59 │      56 │      0 │      53 │  1.47e-03 │ first_order │
+│     87 │     dixmaanh │     99 │  1.00e+00 │  7.37e-06 │        66 │      63 │      0 │      59 │  1.64e-03 │ first_order │
+│     88 │     dixmaani │     99 │  1.00e+00 │  8.73e-06 │       240 │     239 │      0 │     232 │  5.65e-03 │ first_order │
+│     89 │     dixmaanj │     99 │  1.00e+00 │  9.19e-06 │       261 │     259 │      0 │     250 │  6.52e-03 │ first_order │
+│     90 │     dixmaank │     99 │  1.00e+00 │  9.28e-06 │       209 │     207 │      0 │     197 │  5.28e-03 │ first_order │
+│     91 │     dixmaanl │     99 │  1.00e+00 │  7.52e-06 │       181 │     178 │      0 │     172 │  5.02e-03 │ first_order │
+│     92 │     dixmaanm │     99 │  1.00e+00 │  9.42e-06 │       365 │     364 │      0 │     353 │  8.40e-03 │ first_order │
+│     93 │     dixmaann │     99 │  1.00e+00 │  9.67e-06 │       324 │     323 │      0 │     313 │  8.10e-03 │ first_order │
+│     94 │     dixmaano │     99 │  1.00e+00 │  9.69e-06 │       225 │     223 │      0 │     217 │  5.59e-03 │ first_order │
+│     95 │     dixmaanp │     99 │  1.00e+00 │  7.83e-06 │       307 │     304 │      0 │     295 │  7.82e-03 │ first_order │
+│     96 │     dixon3dq │    100 │  1.79e-08 │  8.88e-06 │       468 │     468 │      0 │     457 │  2.00e-03 │ first_order │
 │     97 │      dqdrtic │    100 │  6.55e-16 │  5.54e-07 │        22 │      16 │      0 │      13 │  5.70e-05 │ first_order │
-│     98 │       dqrtic │    100 │  8.25e-08 │  6.42e-06 │        44 │      34 │      0 │      32 │  3.50e-04 │ first_order │
-│    100 │      edensch │    100 │  6.03e+02 │  1.39e-06 │        27 │      25 │      0 │      21 │  6.93e-04 │ first_order │
-│    101 │          eg2 │    100 │ -9.89e+01 │  1.01e-10 │        10 │       6 │      0 │       4 │  1.30e-04 │ first_order │
-│    103 │      engval1 │    100 │  1.09e+02 │  4.65e-06 │        30 │      26 │      0 │      21 │  4.92e-04 │ first_order │
-│    104 │         enso │      9 │  3.94e+02 │  5.33e-06 │        43 │      39 │      0 │      35 │  4.26e-03 │ first_order │
-│    105 │ errinros_mod │    100 │  3.88e+01 │  7.62e-06 │       538 │     498 │      0 │     369 │  7.62e-03 │ first_order │
-│    106 │     extrosnb │    100 │  1.03e-13 │  8.20e-06 │        42 │      36 │      0 │      33 │  4.02e-04 │ first_order │
-│    107 │     fletcbv2 │    100 │ -5.14e-01 │  9.20e-06 │       197 │     197 │      0 │     188 │  3.35e-03 │ first_order │
+│     98 │       dqrtic │    100 │  8.25e-08 │  6.42e-06 │        44 │      34 │      0 │      32 │  3.57e-04 │ first_order │
+│    100 │      edensch │    100 │  6.03e+02 │  1.39e-06 │        27 │      25 │      0 │      21 │  6.26e-04 │ first_order │
+│    101 │          eg2 │    100 │ -9.89e+01 │  1.01e-10 │        10 │       6 │      0 │       4 │  1.33e-04 │ first_order │
+│    103 │      engval1 │    100 │  1.09e+02 │  4.65e-06 │        30 │      26 │      0 │      21 │  5.02e-04 │ first_order │
+│    104 │         enso │      9 │  3.94e+02 │  5.33e-06 │        43 │      39 │      0 │      35 │  4.33e-03 │ first_order │
+│    105 │ errinros_mod │    100 │  3.88e+01 │  7.62e-06 │       538 │     498 │      0 │     369 │  7.73e-03 │ first_order │
+│    106 │     extrosnb │    100 │  1.03e-13 │  8.20e-06 │        42 │      36 │      0 │      33 │  4.16e-04 │ first_order │
+│    107 │     fletcbv2 │    100 │ -5.14e-01 │  9.20e-06 │       197 │     197 │      0 │     188 │  3.41e-03 │ first_order │
 │    108 │ fletcbv3_mod │    100 │ -2.03e+00 │  5.27e-06 │        38 │      34 │      0 │      28 │  1.18e-03 │ first_order │
-│    109 │     fletchcr │    100 │  1.59e-12 │  9.03e-06 │       493 │     488 │      0 │     471 │  6.53e-03 │ first_order │
-│    110 │     fminsrf2 │    100 │  1.00e+02 │  5.88e-06 │       156 │     155 │      0 │     149 │  4.14e-03 │ first_order │
-│    111 │     freuroth │    100 │  5.98e+03 │  6.06e-06 │        36 │      30 │      0 │      27 │  1.21e-03 │ first_order │
-│    112 │       gauss1 │      8 │  6.58e+02 │  2.35e-04 │    233695 │  233171 │      0 │  154795 │  3.00e+01 │    max_time │
-│    113 │       gauss2 │      8 │  6.24e+02 │  1.10e-04 │    233697 │  233121 │      0 │  122397 │  3.00e+01 │    max_time │
-│    114 │       gauss3 │      8 │  6.22e+02 │  3.57e-04 │    228502 │  227024 │      0 │  120096 │  3.00e+01 │    max_time │
-│    116 │     genhumps │    100 │  1.81e-10 │  8.09e-06 │      1004 │     929 │      0 │     779 │  3.87e-02 │ first_order │
-│    117 │      genrose │    100 │  1.00e+00 │  9.14e-06 │       278 │     274 │      0 │     254 │  4.41e-03 │ first_order │
-│    118 │ genrose_nash │    100 │  1.00e+00 │  7.45e-06 │       324 │     309 │      0 │     279 │  5.16e-03 │ first_order │
-│    120 │        hahn1 │      7 │  2.78e+04 │  2.07e-10 │         2 │       2 │      0 │       1 │  1.23e-04 │ first_order │
-│    286 │    indef_mod │    100 │ -9.80e+03 │  6.65e-06 │       131 │     127 │      0 │     107 │  3.59e-03 │ first_order │
-│    287 │     integreq │    100 │  3.70e-11 │  8.83e-06 │         6 │       6 │      0 │       5 │  4.99e-03 │ first_order │
-│    289 │       kirby2 │      5 │  1.95e+00 │  1.39e-03 │   1060279 │ 1059707 │      0 │  538035 │  3.00e+01 │    max_time │
-│    291 │     lanczos1 │      6 │  2.15e-06 │  8.10e-06 │       110 │     105 │      0 │      86 │  1.10e-03 │ first_order │
-│    292 │     lanczos2 │      6 │  2.16e-06 │  9.31e-06 │        98 │      88 │      0 │      71 │  9.17e-04 │ first_order │
-│    293 │     lanczos3 │      6 │  2.17e-06 │  1.61e-06 │       144 │     126 │      0 │     110 │  1.37e-03 │ first_order │
-│    294 │      liarwhd │    100 │  4.65e-16 │  8.77e-07 │        33 │      23 │      0 │      19 │  6.36e-04 │ first_order │
-│    302 │        mgh17 │      5 │  5.52e-02 │  7.40e-07 │        38 │      30 │      0 │      22 │  1.70e-04 │ first_order │
-│    307 │       morebv │    100 │  6.02e-07 │  9.27e-06 │      6321 │    6320 │      0 │    6151 │  1.48e-01 │ first_order │
-│    309 │        ncb20 │    100 │  1.67e+02 │  9.09e-06 │      1131 │    1117 │      0 │    1029 │  1.49e-01 │ first_order │
-│    310 │       ncb20b │    100 │  1.97e+02 │  9.75e-06 │      2719 │    2710 │      0 │    2504 │  4.37e-01 │ first_order │
-│    312 │     noncvxu2 │    100 │  2.32e+02 │  9.20e-06 │       570 │     568 │      0 │     554 │  1.86e-02 │ first_order │
-│    313 │     noncvxun │    100 │  2.32e+02 │  7.46e-06 │       302 │     300 │      0 │     289 │  9.73e-03 │ first_order │
-│    314 │       nondia │    100 │  1.04e-14 │  1.90e-06 │        35 │      25 │      0 │      20 │  3.16e-04 │ first_order │
-│    315 │     nondquar │    100 │  2.65e-06 │  7.91e-06 │      1739 │    1671 │      0 │    1508 │  1.65e-02 │ first_order │
-│    316 │     osborne1 │      5 │  2.34e-02 │  9.67e-02 │   3202544 │ 3197870 │      0 │ 2876291 │  3.00e+01 │    max_time │
-│    317 │     osborne2 │     11 │  2.01e-02 │  3.27e-06 │       184 │     181 │      0 │     163 │  7.73e-03 │ first_order │
-│    318 │     palmer1c │      8 │  2.58e+00 │  3.61e-01 │  99930746 │ 7696955 │      0 │ 3852392 │  3.00e+01 │    max_time │
-│    319 │     palmer1d │      7 │  3.33e-01 │  2.01e-01 │ 104641351 │ 8059761 │      0 │ 4034000 │  3.00e+01 │    max_time │
-│    320 │     palmer2c │      8 │  7.02e-02 │  1.29e-02 │  99397123 │ 7652693 │      0 │ 3828957 │  3.00e+01 │    max_time │
-│    321 │     palmer3c │      8 │  7.82e-02 │  7.03e-03 │  99921986 │ 7690004 │      0 │ 3846454 │  3.00e+01 │    max_time │
-│    322 │     palmer4c │      8 │  1.77e-01 │  1.94e-02 │  99639606 │ 7668908 │      0 │ 3836164 │  3.00e+01 │    max_time │
-│    323 │     palmer5c │      6 │  1.06e+00 │  4.58e-06 │        17 │      16 │      0 │      14 │  5.20e-05 │ first_order │
-│    325 │     palmer6c │      8 │  4.80e-02 │  2.23e-03 │  96589544 │ 8053812 │      0 │ 4028741 │  3.00e+01 │    max_time │
-│    326 │     palmer7c │      8 │  8.04e-01 │  1.07e-01 │  99831994 │ 7692188 │      0 │ 3850945 │  3.00e+01 │    max_time │
-│    327 │     palmer8c │      8 │  2.94e-01 │  2.54e-02 │  98530765 │ 7889147 │      0 │ 3947257 │  3.00e+01 │    max_time │
-│    328 │     penalty1 │    100 │  4.51e-04 │  9.13e-06 │       143 │     126 │      0 │     100 │  1.68e-03 │ first_order │
-│    329 │     penalty2 │    100 │  9.71e+04 │  6.47e-06 │        86 │      73 │      0 │      68 │  3.32e-03 │ first_order │
-│    330 │     penalty3 │    100 │  1.00e+00 │  2.01e+00 │    761905 │   61024 │      0 │   30536 │  3.00e+01 │    max_time │
-│    336 │     powellsg │    100 │  2.64e-09 │  4.85e-06 │        66 │      55 │      0 │      47 │  7.74e-04 │ first_order │
-│    337 │        power │    100 │  1.59e-08 │  8.09e-06 │        61 │      47 │      0 │      45 │  3.28e-04 │ first_order │
-│    338 │       quartc │    100 │  8.25e-08 │  6.42e-06 │        44 │      34 │      0 │      32 │  3.53e-04 │ first_order │
-│    344 │      sbrybnd │    100 │  7.64e+00 │  1.83e-03 │    579199 │  579180 │      0 │  552782 │  3.00e+01 │    max_time │
-│    345 │     schmvett │    100 │ -2.94e+02 │  4.59e-06 │        23 │      23 │      0 │      20 │  1.03e-03 │ first_order │
-│    346 │      scosine │    100 │ -1.99e+01 │  5.17e+16 │    637622 │   91099 │      0 │   30372 │  3.00e+01 │    max_time │
-│    347 │      sinquad │    100 │  1.95e-10 │  9.44e-06 │        83 │      77 │      0 │      68 │  1.83e-03 │ first_order │
-│    348 │     sparsine │    100 │  3.84e-12 │  9.54e-06 │       463 │     449 │      0 │     424 │  2.99e-02 │ first_order │
-│    349 │     sparsqur │    100 │  2.35e-08 │  8.11e-06 │        30 │      25 │      0 │      23 │  8.24e-04 │ first_order │
-│    350 │     spmsrtls │    100 │  5.65e-11 │  7.86e-06 │        71 │      71 │      0 │      66 │  1.51e-03 │ first_order │
-│    351 │     srosenbr │    100 │  2.86e-16 │  7.89e-07 │        52 │      45 │      0 │      39 │  5.30e-04 │ first_order │
-│    361 │      thurber │      7 │  2.82e+03 │  7.96e-05 │   2790017 │ 2789655 │      0 │ 2494193 │  3.00e+01 │    max_time │
-│    362 │     tointgss │    100 │  9.71e+00 │  9.77e-06 │        18 │      18 │      0 │      14 │  6.04e-04 │ first_order │
-│    363 │     tquartic │    100 │  3.71e-20 │  7.91e-09 │        26 │      24 │      0 │      18 │  3.05e-04 │ first_order │
-│    368 │       tridia │    100 │  1.56e-12 │  9.72e-06 │       201 │     197 │      0 │     188 │  8.42e-04 │ first_order │
-│    369 │       vardim │    100 │ -5.22e-08 │  1.42e-09 │        68 │      44 │      0 │      39 │  1.01e-02 │ first_order │
-│    370 │     vibrbeam │      8 │  5.28e+00 │  1.29e-03 │   2767410 │ 2762632 │      0 │ 1768234 │  3.00e+01 │    max_time │
-│    371 │       watson │     31 │      -Inf │ 9.11e+304 │         7 │       7 │      0 │       1 │  5.52e-03 │   unbounded │
-│    372 │        woods │    100 │  9.04e-14 │  5.09e-06 │        85 │      76 │      0 │      61 │  2.39e-03 │ first_order │
+│    109 │     fletchcr │    100 │  1.59e-12 │  9.03e-06 │       493 │     488 │      0 │     471 │  6.48e-03 │ first_order │
+│    110 │     fminsrf2 │    100 │  1.00e+02 │  5.88e-06 │       156 │     155 │      0 │     149 │  4.38e-03 │ first_order │
+│    111 │     freuroth │    100 │  5.98e+03 │  6.06e-06 │        36 │      30 │      0 │      27 │  1.32e-03 │ first_order │
+│    112 │       gauss1 │      8 │  6.58e+02 │  2.33e-04 │    227159 │  226635 │      0 │  150471 │  3.00e+01 │    max_time │
+│    113 │       gauss2 │      8 │  6.24e+02 │  1.11e-04 │    228444 │  227868 │      0 │  119631 │  3.00e+01 │    max_time │
+│    114 │       gauss3 │      8 │  6.22e+02 │  2.98e-04 │    225880 │  224402 │      0 │  118749 │  3.00e+01 │    max_time │
+│    116 │     genhumps │    100 │  1.81e-10 │  8.09e-06 │      1004 │     929 │      0 │     779 │  3.91e-02 │ first_order │
+│    117 │      genrose │    100 │  1.00e+00 │  9.14e-06 │       278 │     274 │      0 │     254 │  4.55e-03 │ first_order │
+│    118 │ genrose_nash │    100 │  1.00e+00 │  7.45e-06 │       324 │     309 │      0 │     279 │  5.14e-03 │ first_order │
+│    120 │        hahn1 │      7 │  2.78e+04 │  2.07e-10 │         2 │       2 │      0 │       1 │  1.21e-04 │ first_order │
+│    287 │    indef_mod │    100 │ -9.80e+03 │  6.65e-06 │       131 │     127 │      0 │     107 │  3.72e-03 │ first_order │
+│    288 │     integreq │    100 │  3.70e-11 │  8.83e-06 │         6 │       6 │      0 │       5 │  5.09e-03 │ first_order │
+│    290 │       kirby2 │      5 │  1.95e+00 │  1.36e-03 │   1047312 │ 1046740 │      0 │  531227 │  3.00e+01 │    max_time │
+│    292 │     lanczos1 │      6 │  2.15e-06 │  8.10e-06 │       110 │     105 │      0 │      86 │  1.12e-03 │ first_order │
+│    293 │     lanczos2 │      6 │  2.16e-06 │  9.31e-06 │        98 │      88 │      0 │      71 │  9.50e-04 │ first_order │
+│    294 │     lanczos3 │      6 │  2.17e-06 │  1.61e-06 │       144 │     126 │      0 │     110 │  1.39e-03 │ first_order │
+│    295 │      liarwhd │    100 │  4.65e-16 │  8.77e-07 │        33 │      23 │      0 │      19 │  6.44e-04 │ first_order │
+│    303 │        mgh17 │      5 │  5.52e-02 │  7.40e-07 │        38 │      30 │      0 │      22 │  1.74e-04 │ first_order │
+│    308 │       morebv │    100 │  6.02e-07 │  9.27e-06 │      6321 │    6320 │      0 │    6151 │  1.51e-01 │ first_order │
+│    310 │        ncb20 │    100 │  1.67e+02 │  9.09e-06 │      1131 │    1117 │      0 │    1029 │  1.67e-01 │ first_order │
+│    311 │       ncb20b │    100 │  1.97e+02 │  9.75e-06 │      2719 │    2710 │      0 │    2504 │  4.22e-01 │ first_order │
+│    313 │     noncvxu2 │    100 │  2.32e+02 │  9.20e-06 │       570 │     568 │      0 │     554 │  1.87e-02 │ first_order │
+│    314 │     noncvxun │    100 │  2.32e+02 │  7.46e-06 │       302 │     300 │      0 │     289 │  1.00e-02 │ first_order │
+│    315 │       nondia │    100 │  1.04e-14 │  1.90e-06 │        35 │      25 │      0 │      20 │  3.16e-04 │ first_order │
+│    316 │     nondquar │    100 │  2.65e-06 │  7.91e-06 │      1739 │    1671 │      0 │    1508 │  1.64e-02 │ first_order │
+│    317 │     osborne1 │      5 │  2.34e-02 │  9.65e-02 │   3184243 │ 3179593 │      0 │ 2859839 │  3.00e+01 │    max_time │
+│    318 │     osborne2 │     11 │  2.01e-02 │  3.27e-06 │       184 │     181 │      0 │     163 │  7.92e-03 │ first_order │
+│    319 │     palmer1c │      8 │  2.58e+00 │  3.61e-01 │ 102018598 │ 7857559 │      0 │ 3932694 │  3.00e+01 │    max_time │
+│    320 │     palmer1d │      7 │  3.33e-01 │  2.01e-01 │ 107917351 │ 8311761 │      0 │ 4160000 │  3.00e+01 │    max_time │
+│    321 │     palmer2c │      8 │  7.02e-02 │  1.29e-02 │ 101751813 │ 7833823 │      0 │ 3919522 │  3.00e+01 │    max_time │
+│    322 │     palmer3c │      8 │  7.82e-02 │  7.03e-03 │ 102607812 │ 7896606 │      0 │ 3949755 │  3.00e+01 │    max_time │
+│    323 │     palmer4c │      8 │  1.77e-01 │  1.94e-02 │ 102266152 │ 7870950 │      0 │ 3937185 │  3.00e+01 │    max_time │
+│    324 │     palmer5c │      6 │  1.06e+00 │  4.58e-06 │        17 │      16 │      0 │      14 │  4.60e-05 │ first_order │
+│    326 │     palmer6c │      8 │  4.80e-02 │  2.23e-03 │  98488280 │ 8212040 │      0 │ 4107855 │  3.00e+01 │    max_time │
+│    327 │     palmer7c │      8 │  8.04e-01 │  1.07e-01 │ 102495460 │ 7897070 │      0 │ 3953386 │  3.00e+01 │    max_time │
+│    328 │     palmer8c │      8 │  2.94e-01 │  2.54e-02 │ 100627315 │ 8056871 │      0 │ 4031119 │  3.00e+01 │    max_time │
+│    329 │     penalty1 │    100 │  4.51e-04 │  9.13e-06 │       143 │     126 │      0 │     100 │  1.69e-03 │ first_order │
+│    330 │     penalty2 │    100 │  9.71e+04 │  6.47e-06 │        86 │      73 │      0 │      68 │  3.39e-03 │ first_order │
+│    331 │     penalty3 │    100 │  1.00e+00 │  2.01e+00 │    760530 │   60914 │      0 │   30481 │  3.00e+01 │    max_time │
+│    337 │     powellsg │    100 │  2.64e-09 │  4.85e-06 │        66 │      55 │      0 │      47 │  7.84e-04 │ first_order │
+│    338 │        power │    100 │  1.59e-08 │  8.09e-06 │        61 │      47 │      0 │      45 │  3.47e-04 │ first_order │
+│    339 │       quartc │    100 │  8.25e-08 │  6.42e-06 │        44 │      34 │      0 │      32 │  3.56e-04 │ first_order │
+│    345 │      sbrybnd │    100 │  7.64e+00 │  7.47e-03 │    551429 │  551410 │      0 │  526295 │  3.00e+01 │    max_time │
+│    346 │     schmvett │    100 │ -2.94e+02 │  4.59e-06 │        23 │      23 │      0 │      20 │  1.08e-03 │ first_order │
+│    347 │      scosine │    100 │ -1.99e+01 │  5.17e+16 │    631343 │   90202 │      0 │   30073 │  3.00e+01 │    max_time │
+│    348 │      sinquad │    100 │  1.95e-10 │  9.44e-06 │        83 │      77 │      0 │      68 │  1.85e-03 │ first_order │
+│    349 │     sparsine │    100 │  3.84e-12 │  9.54e-06 │       463 │     449 │      0 │     424 │  3.05e-02 │ first_order │
+│    350 │     sparsqur │    100 │  2.35e-08 │  8.11e-06 │        30 │      25 │      0 │      23 │  8.10e-04 │ first_order │
+│    351 │     spmsrtls │    100 │  5.65e-11 │  7.86e-06 │        71 │      71 │      0 │      66 │  1.61e-03 │ first_order │
+│    352 │     srosenbr │    100 │  2.86e-16 │  7.89e-07 │        52 │      45 │      0 │      39 │  5.38e-04 │ first_order │
+│    362 │      thurber │      7 │  2.82e+03 │  8.35e-05 │   2669507 │ 2669145 │      0 │ 2386500 │  3.00e+01 │    max_time │
+│    363 │     tointgss │    100 │  9.71e+00 │  9.77e-06 │        18 │      18 │      0 │      14 │  6.31e-04 │ first_order │
+│    364 │     tquartic │    100 │  3.71e-20 │  7.91e-09 │        26 │      24 │      0 │      18 │  3.14e-04 │ first_order │
+│    369 │       tridia │    100 │  1.56e-12 │  9.72e-06 │       201 │     197 │      0 │     188 │  8.32e-04 │ first_order │
+│    370 │       vardim │    100 │ -5.22e-08 │  1.42e-09 │        68 │      44 │      0 │      39 │  1.02e-02 │ first_order │
+│    371 │     vibrbeam │      8 │  5.28e+00 │  1.31e-03 │   2665539 │ 2660761 │      0 │ 1703707 │  3.00e+01 │    max_time │
+│    372 │       watson │     31 │      -Inf │ 9.11e+304 │         7 │       7 │      0 │       1 │  3.55e-03 │   unbounded │
+│    373 │        woods │    100 │  9.04e-14 │  5.09e-06 │        85 │      76 │      0 │      61 │  1.51e-03 │ first_order │
 └────────┴──────────────┴────────┴───────────┴───────────┴───────────┴─────────┴────────┴─────────┴───────────┴─────────────┘
first_order(df) = df.status .== :first_order
 unbounded(df) = df.status .== :unbounded
 solved(df) = first_order(df) .| unbounded(df)
@@ -254,6 +254,6 @@
 using Plots
 gr()
 
-profile_solvers(stats, costs, costnames)
Example block output

It is also possible to select problems when initializing the problem list by filtering OptimizationProblems.meta:

meta = OptimizationProblems.meta
+profile_solvers(stats, costs, costnames)
Example block output

It is also possible to select problems when initializing the problem list by filtering OptimizationProblems.meta:

meta = OptimizationProblems.meta
 problem_list = meta[(meta.ncon .== 0) .& .!meta.has_bounds .& (5 .<= meta.nvar .<= 100), :name]
-problems = (MathOptNLPModel(eval(Meta.parse(problem))(), name=problem) for problem ∈ problem_list)
+problems = (MathOptNLPModel(eval(Meta.parse(problem))(), name=problem) for problem ∈ problem_list) diff --git a/dev/contributing/index.html b/dev/contributing/index.html index 4051b6bb..fba40091 100644 --- a/dev/contributing/index.html +++ b/dev/contributing/index.html @@ -29,4 +29,4 @@ # define x0 # nlp = ADNLPModels.ADNLPModel(f, x0, name = "function_name"; kwargs...) return nlp -end +end diff --git a/dev/index.html b/dev/index.html index bf25f80e..1e352ab4 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,4 +1,4 @@ Home · OptimizationProblems.jl
GitHub

OptimizationProblems.jl

This package provides a collection of optimization problems in JuMP and ADNLPModels syntax.

Installing

OptimizationProblems can be installed and tested through the Julia package manager:

julia> ]
 pkg> add OptimizationProblems
-pkg> test OptimizationProblems

How to cite

If you use OptimizationProblems.jl in your work, please cite using the format given in CITATION.cff.

Bug reports and discussions

If you think you found a bug, feel free to open an issue. Focused suggestions and requests can also be opened as issues. Before opening a pull request, start an issue or a discussion on the topic, please.

If you want to ask a question not suited for a bug report, feel free to start a discussion here. This forum is for general discussion about this repository and the JuliaSmoothOptimizers, so questions about any of our packages are welcome.

+pkg> test OptimizationProblems

How to cite

If you use OptimizationProblems.jl in your work, please cite using the format given in CITATION.cff.

Bug reports and discussions

If you think you found a bug, feel free to open an issue. Focused suggestions and requests can also be opened as issues. Before opening a pull request, start an issue or a discussion on the topic, please.

If you want to ask a question not suited for a bug report, feel free to start a discussion here. This forum is for general discussion about this repository and the JuliaSmoothOptimizers, so questions about any of our packages are welcome.

diff --git a/dev/meta/index.html b/dev/meta/index.html index 6b6ce5df..e82e9dcf 100644 --- a/dev/meta/index.html +++ b/dev/meta/index.html @@ -40,7 +40,7 @@ ├ num_variables: 10 ├ num_constraints: 0 └ Names registered in the model - └ :x

Global meta

This package collects all the metadata in a single DataFrame.

OptimizationProblems.meta
373×17 DataFrame
Rownvarvariable_nvarnconvariable_nconminimizenamehas_equalities_onlyhas_inequalities_onlyhas_boundshas_fixed_variablesobjtypecontypebest_known_lower_boundbest_known_upper_boundis_feasibledefined_everywhereorigin
Int64BoolInt64BoolBoolStringBoolBoolBoolBoolSymbolSymbolRealRealBool?Bool?Symbol
11false0falsetrueAMPGO02falsefalsefalsefalseotherunconstrained-Inf0.839498truemissingunknown
21false0falsetrueAMPGO03falsefalsefalsefalseotherunconstrained-Inf2.88961truemissingunknown
31false0falsetrueAMPGO04falsefalsefalsefalseotherunconstrained-Inf-2.5666truemissingunknown
41false0falsetrueAMPGO05falsefalsefalsefalseotherunconstrained-Inf-0.0truemissingunknown
51false0falsetrueAMPGO06falsefalsefalsefalseotherunconstrained-Inf3.5177e-43truemissingunknown
61false0falsetrueAMPGO07falsefalsefalsefalseotherunconstrained-Inf2.56475truemissingunknown
71false0falsetrueAMPGO08falsefalsefalsefalseotherunconstrained-Inf-2.0928truemissingunknown
81false0falsetrueAMPGO09falsefalsefalsefalseotherunconstrained-Inf0.921136truemissingunknown
91false0falsetrueAMPGO10falsefalsefalsefalseotherunconstrained-Inf-0.0truemissingunknown
101false0falsetrueAMPGO11falsefalsefalsefalseotherunconstrained-Inf-1.0truemissingunknown
111false0falsetrueAMPGO12falsefalsefalsefalseotherunconstrained-Inf1.0truemissingunknown
121false0falsetrueAMPGO13falsefalsefalsefalseotherunconstrained-1.5874-1.5874truemissingunknown
131false0falsetrueAMPGO14falsefalsefalsefalseotherunconstrained-Inf-0.0truemissingunknown
141false0falsetrueAMPGO15falsefalsefalsefalseotherunconstrained-Inf2.15385truemissingunknown
151false0falsetrueAMPGO18falsefalsefalsefalseotherunconstrained-Inf4.0truemissingunknown
161false0falsetrueAMPGO20falsefalsefalsefalseotherunconstrained-Inf3.92246e-43truemissingunknown
171false0falsetrueAMPGO21falsefalsefalsefalseotherunconstrained-Inf0.0truemissingunknown
181false0falsetrueAMPGO22falsefalsefalsefalseotherunconstrained-Inf1.0truemissingunknown
193false1falsetrueBOX2truefalsefalsefalseotherlinear-Inf0.942284truemissingunknown
203false0falsetrueBOX3falsefalsefalsefalseotherunconstrained-Inf662.868truemissingunknown
211false0falsetrueDus2_1falsefalsefalsefalseotherunconstrained-Inf7.38906truemissingunknown
221false0falsetrueDus2_3falsefalsefalsefalseotherunconstrained-Inf0.972973truemissingunknown
231false0falsetrueDus2_9falsefalsefalsefalseotherunconstrained-Inf1.0truemissingunknown
241false0falsetrueDuscubefalsefalsefalsefalseotherunconstrained-Inf-108.0truemissingunknown
2591true0falsetrueNZF1falsefalsefalsefalseleast_squaresunconstrained-Inf34698.4truemissingunknown
261false0falsetrueShpak1falsefalsefalsefalseotherunconstrained-Inf0.839498truemissingunknown
271false0falsetrueShpak2falsefalsefalsefalseotherunconstrained-Inf-0.747036truemissingunknown
281false0falsetrueShpak3falsefalsefalsefalseotherunconstrained-Inf2.63055truemissingunknown
291false0falsetrueShpak4falsefalsefalsefalseotherunconstrained-Inf-0.677439truemissingunknown
301false0falsetrueShpak5falsefalsefalsefalseotherunconstrained-Inf-1.00842truemissingunknown
311false0falsetrueShpak6falsefalsefalsefalseotherunconstrained-Inf-1.25667truemissingunknown
328false5falsetrueaircrftatruefalsetruetrueothergeneral-Inf0truemissingunknown
334false3falsetrueallinitfalsefalsefalsefalseotherlinear-InfInfmissingmissingunknown
344false4falsetrueallinitcfalsefalsefalsefalseothergeneral-InfInfmissingmissingunknown
354false0falsetrueallinitufalsefalsefalsefalseotherunconstrained-Inf13.0truemissingunknown
362false3falsetruealsotamefalsefalsefalsefalseothergeneral-InfInfmissingmissingunknown
373false15falsetrueargausstruefalsefalsefalseothergeneral-InfInfmissingmissingunknown
38100true0falsetruearglinafalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
39100true0falsetruearglinbfalsefalsefalsefalseleast_squaresunconstrained-Inf6.85174e13truemissingunknown
40100true0falsetruearglincfalsefalsefalsefalseotherunconstrained-Inf6.38544e13truemissingunknown
41100true0falsetrueargtrigfalsefalsefalsefalseotherunconstrained-Inf0.0truemissingunknown
42100true0falsetruearwheadfalsefalsefalsefalseotherunconstrained-Inf297.0truemissingunknown
4349false15falsetrueavion2truefalsetruefalseleast_squareslinear-InfInfmissingmissingunknown
443false0falsetruebardfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
45100true0falsetruebdqrticfalsefalsefalsefalseleast_squaresunconstrained-Inf21696.0truemissingunknown
462false0falsetruebealefalsefalsefalsefalseleast_squaresunconstrained-Inf14.2031truemissingunknown
47100true0falsetruebearingfalsefalsetruetrueotherunconstrained-Inf15.0662truemissingunknown
483false0falsetruebennett5falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
496false0falsetruebiggs5falsefalsetruetrueotherunconstrained-Inf-3.15621truemissingunknown
506false0falsetruebiggs6falsefalsefalsefalseotherunconstrained-Inf-3.15621truemissingunknown
512false2falsetrueboothtruefalsefalsefalseotherlinear-InfInfmissingmissingunknown
522false0falsetrueboxbodfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
531false1falsetruebqp1varfalsetruefalsefalseotherlinear-Inf0.3125truemissingunknown
54450false360falsetruebritgastruefalsetruefalseothergeneral-InfInfmissingmissingunknown
55100true0falsetruebrownalfalsefalsefalsefalseleast_squaresunconstrained-Inf0.0truemissingunknown
562false0falsetruebrownbsfalsefalsefalsefalseleast_squaresunconstrained-Inf9.99998e11truemissingunknown
574false0falsetruebrowndenfalsefalsefalsefalseleast_squaresunconstrained-Inf7.92669e6truemissingunknown
58100true0falsetruebroyden3dfalsefalsefalsefalseleast_squaresunconstrained-Inf0.0truemissingunknown
59100true0falsetruebroydn7dfalsefalsefalsefalseotherunconstrained-Inf274.204truemissingunknown
60100true0falsetruebrybndfalsefalsefalsefalseleast_squaresunconstrained-Inf3600.0truemissingunknown
612false1falsetruebt1truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
62100true203truetruecamshapefalsetruetruefalseotherquadratic-InfInfmissingmissingunknown
6399true32truetruecatenarytruefalsetruetruelinearquadratic-InfInfmissingmissingacademic
64100true77truetruechaintruefalsefalsefalseothergeneral-InfInfmissingmissingunknown
65100true0falsetruechainwoofalsefalsefalsefalseotherunconstrained-Inf3.71954e5truemissingunknown
6696true96truetruechanneltruefalsefalsefalseothergeneral-InfInfmissingmissingunknown
67100true0falsetruechnrosnb_modfalsefalsefalsefalseotherunconstrained-Inf17637.9truemissingunknown
683false0falsetruechwirut1falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
693false0falsetruechwirut2falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
702false0falsetrueclifffalsefalsefalsefalseotherunconstrained-Inf4.85165e8truemissingunknown
7199true64truetrueclnlbeamtruefalsetruefalseothergeneral-Inf350.0truemissingunknown
72100true0falsetrueclplateafalsefalsefalsefalseotherunconstrained-Inf0.0truemissingunknown
73100true0falsetrueclplatebfalsefalsefalsefalseotherunconstrained-Inf0.0truemissingunknown
74100true0falsetrueclplatecfalsefalsefalsefalseotherunconstrained-Inf0.0truemissingunknown
75100true50truetruecontrolinvestmenttruefalsetruefalseothergeneral-Inf-0.98truemissingunknown
76100true0falsetruecosinefalsefalsefalsefalseotherunconstrained-Inf86.8807truemissingunknown
77100true0falsetruecragglvyfalsefalsefalsefalseotherunconstrained-Inf52823.1truemissingunknown
78100true0falsetruecragglvy2falsefalsefalsefalseotherunconstrained-Inf52823.1truemissingunknown
79100true0falsetruecurlyfalsefalsefalsefalseotherunconstrained-Inf-0.00623722truemissingunknown
80100true0falsetruecurly10falsefalsefalsefalseotherunconstrained-Inf-0.00623722truemissingunknown
81100true0falsetruecurly20falsefalsefalsefalseotherunconstrained-Inf-0.0129654truemissingunknown
82100true0falsetruecurly30falsefalsefalsefalseotherunconstrained-Inf-0.020383truemissingunknown
832false0falsetruedanwoodfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
8499true0falsetruedixmaanefalsefalsefalsefalseotherunconstrained-Inf731.833truemissingunknown
8599true0falsetruedixmaanffalsefalsefalsefalseotherunconstrained-Inf1348.42truemissingunknown
8699true0falsetruedixmaangfalsefalsefalsefalseotherunconstrained-Inf2495.83truemissingunknown
8799true0falsetruedixmaanhfalsefalsefalsefalseotherunconstrained-Inf4974.25truemissingunknown
8899true0falsetruedixmaanifalsefalsefalsefalseotherunconstrained-Inf663.646truemissingunknown
8999true0falsetruedixmaanjfalsefalsefalsefalseotherunconstrained-Inf1281.33truemissingunknown
9099true0falsetruedixmaankfalsefalsefalsefalseotherunconstrained-Inf2427.65truemissingunknown
9199true0falsetruedixmaanlfalsefalsefalsefalseotherunconstrained-Inf4903.7truemissingunknown
9299true0falsetruedixmaanmfalsefalsefalsefalseotherunconstrained-Inf314.313truemissingunknown
9399true0falsetruedixmaannfalsefalsefalsefalseotherunconstrained-Inf665.66truemissingunknown
9499true0falsetruedixmaanofalsefalsefalsefalseotherunconstrained-Inf1196.31truemissingunknown
9599true0falsetruedixmaanpfalsefalsefalsefalseotherunconstrained-Inf2342.52truemissingunknown
96100true0falsetruedixon3dqfalsefalsefalsefalseleast_squaresunconstrained-Inf8.0truemissingunknown
97100true0falsetruedqdrticfalsefalsefalsefalseotherunconstrained-Inf177282.0truemissingunknown
98100true0falsetruedqrticfalsefalsefalsefalseotherunconstrained-Inf1.85427e9truemissingunknown
993false0falsetrueeckerle4falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
100100true0falsetrueedenschfalsefalsefalsefalseotherunconstrained-Inf1699.0truemissingunknown
101100true0falsetrueeg2falsefalsefalsefalseotherunconstrained-Inf-83.3056truemissingunknown
10299true33truetrueelectruefalsefalsefalseotherquadratic-InfInfmissingmissingunknown
103100true0falsetrueengval1falsefalsefalsefalseotherunconstrained-Inf5841.0truemissingunknown
1049false0falsetrueensofalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
105100true0falsetrueerrinros_modfalsefalsefalsefalseleast_squaresunconstrained-Inf3.13991e5truemissingunknown
106100true0falsetrueextrosnbfalsefalsefalsefalseotherunconstrained-Inf39604.0truemissingunknown
107100true0falsetruefletcbv2falsefalsefalsefalseotherunconstrained-Inf-0.513108truemissingunknown
108100true0falsetruefletcbv3_modfalsefalsefalsefalseotherunconstrained-Inf-0.0187925truemissingunknown
109100true0falsetruefletchcrfalsefalsefalsefalseotherunconstrained-Inf9900.0truemissingunknown
110100false0falsetruefminsrf2falsefalsefalsefalseotherunconstrained-Inf2504.27truemissingunknown
111100true0falsetruefreurothfalsefalsefalsefalseleast_squaresunconstrained-Inf99556.5truemissingunknown
1128false0falsetruegauss1falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
1138false0falsetruegauss2falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
1148false0falsetruegauss3falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
1153false0falsetruegaussianfalsefalsefalsefalseleast_squaresunconstrained-Inf3.88811e-6truemissingunknown
116100true0falsetruegenhumpsfalsefalsefalsefalseotherunconstrained-Inf2.53684e6truemissingunknown
117100true0falsetruegenrosefalsefalsefalsefalseotherunconstrained-Inf405.106truemissingunknown
118100true0falsetruegenrose_nashfalsefalsefalsefalseotherunconstrained-Inf404.126truemissingunknown
1193false0falsetruegulffalsefalsefalsefalseleast_squaresunconstrained-Inf0.0truemissingunknown
1207false0falsetruehahn1falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
1213false0falsetruehelicalfalsefalsefalsefalseotherunconstrained-Inf0.0truemissingunknown
12298true68truetruehovercraft1dtruefalsefalsefalseleast_squareslinear-InfInfmissingmissingunknown
1232false0falsetruehs1falsefalsetruefalseotherunconstrained-Inf909.0truemissingunknown
1242false1falsetruehs10falsetruefalsefalseothergeneral-InfInfmissingmissingunknown
1257false4falsetruehs100falsetruefalsefalseothergeneral-Inf714.0truemissingunknown
1267false5falsetruehs101falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1277false5falsetruehs102falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1287false5falsetruehs103falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1298false5falsetruehs104falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1308false1falsetruehs105falsetruetruefalseotherlinear-InfInfmissingmissingunknown
1318false6falsetruehs106falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1329false6falsetruehs107truefalsetruefalseothergeneral-InfInfmissingmissingunknown
1339false12falsetruehs108falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1349false9falsetruehs109falsefalsetruefalseothergeneral-InfInfmissingmissingunknown
1352false1falsetruehs11falsetruefalsefalseothergeneral-InfInfmissingmissingunknown
13610false0falsetruehs110falsefalsetruefalseotherunconstrained-Inf-43.1343truemissingunknown
13710false3falsetruehs111truefalsetruefalseothergeneral-InfInfmissingmissingunknown
13810false3falsetruehs112truefalsetruefalseotherlinear-InfInfmissingmissingunknown
13910false8falsetruehs113falsetruefalsefalseothergeneral-Inf753.0truemissingunknown
14010false11falsetruehs114falsefalsetruefalseothergeneral-InfInfmissingmissingunknown
14113false15falsetruehs116falsetruetruefalseothergeneral-InfInfmissingmissingunknown
14215false5falsetruehs117falsetruetruefalseothergeneral-Inf2400.11truemissingunknown
14315false17falsetruehs118falsetruetruefalseotherlinear-Inf942.716truemissingunknown
14416false8falsetruehs119truefalsetruefalseotherlinear-InfInfmissingmissingunknown
1452false1falsetruehs12falsetruefalsefalseothergeneral-Inf0.0truemissingunknown
1462false1falsetruehs13falsetruetruefalseleast_squaresgeneral-InfInfmissingmissingunknown
1472false2falsetruehs14falsefalsefalsefalseleast_squaresgeneral-InfInfmissingmissingunknown
1482false2falsetruehs15falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1492false2falsetruehs16falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1502false2falsetruehs17falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1512false2falsetruehs18falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1522false2falsetruehs19falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1532false0falsetruehs2falsefalsetruefalseotherunconstrained-Inf909.0truemissingunknown
1542false3falsetruehs20falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1552false0falsetruehs201falsefalsefalsefalseotherunconstrained-Inf45.0truemissingunknown
1562false1falsetruehs21falsetruetruefalseotherlinear-InfInfmissingmissingunknown
1574false2falsetruehs219truefalsefalsefalselineargeneral-1.0-1.0truetrueunknown
1582false2falsetruehs22falsetruefalsefalseleast_squaresgeneral-InfInfmissingmissingunknown
1592false1falsetruehs220truefalsetruefalseothergeneral125000truemissingunknown
1602false1falsetruehs221falsetruetruefalseothergeneral-1-0.25truemissingunknown
1612false1falsetruehs222falsetruetruefalseothergeneral-1.5-1.3truemissingunknown
1622false2falsetruehs223falsetruetruefalseothergeneral-0.834032-0.1truemissingunknown
1632false4falsetruehs224falsetruetruefalseotherlinear-304-304truemissingunknown
1642false5falsetruehs225falsetruefalsefalseothergeneral22truemissingunknown
1652false2falsetruehs226falsetruetruefalseothergeneral-0.5-0.5truemissingunknown
1662false2falsetruehs227falsetruefalsefalseothergeneral11truemissingunknown
1672false2falsetruehs228falsetruefalsefalseothergeneral-3-3truemissingunknown
1682false0falsetruehs229falsefalsetruefalseotherunconstrained00truemissingunknown
1692false5falsetruehs23falsetruetruefalseleast_squaresgeneral-InfInfmissingmissingunknown
1702false2falsetruehs230falsetruefalsefalseothergeneral0.3750.375truemissingunknown
1712false2falsetruehs231falsetruefalsefalseotherlinear00truemissingunknown
1722false3falsetruehs232falsetruetruefalseotherlinear-1-1truemissingunknown
1732false1falsetruehs233falsetruefalsefalseotherunconstrained00truemissingunknown
1742false1falsetruehs234falsetruetruefalseothergeneral-0.8-0.8truemissingunknown
1753false1falsetruehs235truefalsefalsefalseothergeneral0.040.04truemissingunknown
1762false2falsetruehs236falsetruetruefalseothergeneral-58.9034-58.9034truemissingunknown
1772false3falsetruehs237falsetruetruefalseothergeneral-58.9034-58.9034truemissingunknown
1782false3falsetruehs238falsetruetruefalseothergeneral-58.9034-58.9034truemissingunknown
1792false1falsetruehs239falsetruetruefalseothergeneral-58.9034-58.9034truemissingunknown
1802false2falsetruehs24falsetruetruefalseotherlinear-Inf-0.0133646truemissingunknown
1813false0falsetruehs240falsefalsefalsefalseotherunconstrained00truemissingunknown
1823false0falsetruehs241falsefalsefalsefalseotherunconstrained00truemissingunknown
1833false0falsetruehs242falsefalsetruefalseotherunconstrained00truemissingunknown
1843false0falsetruehs243falsefalsefalsefalseotherunconstrained0.79660.7966truemissingunknown
1853false0falsetruehs244falsefalsefalsefalseotherunconstrained00truemissingunknown
1863false0falsetruehs245falsefalsefalsefalseotherunconstrained00truemissingunknown
1873false0falsetruehs246falsefalsefalsefalseotherunconstrained00truemissingunknown
1883false2falsetruehs248falsefalsefalsefalseothergeneral-0.8-0.8truemissingunknown
1893false1falsetruehs249falsetruetruefalseothergeneral11truemissingunknown
1903false0falsetruehs25falsefalsetruefalseotherunconstrained-Inf32.835truemissingunknown
1913false2falsetruehs250falsetruetruefalseotherlinear-3300-3300truemissingunknown
1923false1falsetruehs251falsetruetruefalseotherlinear-3456-3456truemissingunknown
1933false1falsetruehs252truefalsefalsefalseotherunconstrained0.040.04truemissingunknown
1943false1falsetruehs253falsetruetruefalseotherlinear87.379487.3794truemissingunknown
1953false2falsetruehs254truefalsetruefalseothergeneral-1.73205-1.73205truemissingunknown
1964false0falsetruehs255falsefalsefalsefalseotherunconstrained00truemissingunknown
1974false0falsetruehs256falsefalsefalsefalseotherunconstrained00truemissingunknown
1984false0falsetruehs257falsefalsetruefalseotherunconstrained00truemissingunknown
1994false0falsetruehs258falsefalsefalsefalseotherunconstrained00truemissingunknown
2004false0falsetruehs259falsefalsefalsefalseotherunconstrained00truemissingunknown
2013false1falsetruehs26truefalsefalsefalseothergeneral-Inf21.16truemissingunknown
2024false0falsetruehs260falsefalsefalsefalseotherunconstrained00truemissingunknown
2034false0falsetruehs261falsefalsefalsefalseotherunconstrained00truemissingunknown
2044false4falsetruehs262falsefalsetruefalseotherlinear-10-10truemissingunknown
2054false4falsetruehs263falsefalsefalsefalseothergeneral-1-1truemissingunknown
2064false3falsetruehs264falsetruefalsefalseothergeneral-44-44truemissingunknown
2074false2falsetruehs265truefalsetruefalseotherlinear0.9747470.974747truemissingunknown
2083false1falsetruehs27truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2093false1falsetruehs28truefalsefalsefalseleast_squareslinear-Inf13.0truemissingunknown
2103false1falsetruehs29falsetruefalsefalseothergeneral-Inf-1.0truemissingunknown
2112false0falsetruehs3falsefalsetruefalseotherunconstrained-Inf1.00081truemissingunknown
2123false1falsetruehs30falsetruetruefalseleast_squaresgeneral-Inf3.0truemissingunknown
2133false1falsetruehs31falsetruetruefalseothergeneral-Inf19.0truemissingunknown
2142false1falsetruehs316truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2152false1falsetruehs317truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2162false1falsetruehs318truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2172false1falsetruehs319truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2183false2falsetruehs32falsefalsetruefalseothergeneral-Inf7.2truemissingunknown
2192false1falsetruehs320truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2202false1falsetruehs321truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2212false1falsetruehs322truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2223false2falsetruehs33falsetruetruefalseothergeneral-Inf-3.0truemissingunknown
2233false2falsetruehs34falsetruetruefalseothergeneral-Inf0.0truemissingunknown
2243false1falsetruehs35falsetruetruefalseotherlinear-Inf2.25truemissingunknown
2253false1falsetruehs36falsetruetruefalseotherlinear-Inf-1000.0truemissingunknown
2263false1falsetruehs37falsetruetruefalseotherlinear-Inf-1000.0truemissingunknown
22710false3falsetruehs378truefalsefalsefalseothergeneral-InfInftruemissingacademic
2284false0falsetruehs38falsefalsetruefalseotherunconstrained-Inf19192.0truemissingunknown
2294false2falsetruehs39truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2302false0falsetruehs4falsefalsetruefalseotherunconstrained-Inf3.32357truemissingunknown
2314false3falsetruehs40truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2324false1falsetruehs41truefalsetruefalseotherlinear-InfInfmissingmissingunknown
2334false2falsetruehs42truefalsefalsefalseleast_squaresgeneral-InfInfmissingmissingunknown
2344false3falsetruehs43falsetruefalsefalseothergeneral-Inf0.0truemissingunknown
2354false6falsetruehs44falsetruetruefalseotherlinear-Inf0.0truemissingunknown
2365false0falsetruehs45falsefalsetruefalseotherunconstrained-Inf1.73333truemissingunknown
2375false2falsetruehs46truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2385false3falsetruehs47truefalsefalsefalseothergeneral-Inf20.7381truemissingunknown
2395false2falsetruehs48truefalsefalsefalseleast_squareslinear-Inf84.0truemissingunknown
2405false2falsetruehs49truefalsefalsefalseotherlinear-Inf266.0truemissingunknown
2412false0falsetruehs5falsefalsetruefalseotherunconstrained-Inf1.0truemissingunknown
2425false3falsetruehs50truefalsefalsefalseotherlinear-Inf7516.0truemissingunknown
2435false3falsetruehs51truefalsefalsefalseleast_squareslinear-Inf8.5truemissingunknown
2445false3falsetruehs52truefalsefalsefalseleast_squareslinear-InfInfmissingmissingunknown
2455false3falsetruehs53truefalsetruefalseleast_squareslinear-InfInfmissingmissingunknown
2466false1falsetruehs54truefalsetruefalseotherlinear-InfInfmissingmissingunknown
2476false6falsetruehs55truefalsetruefalseotherlinear-InfInfmissingmissingunknown
2487false4falsetruehs56truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2492false1falsetruehs57falsetruetruefalseleast_squaresgeneral-Inf0.0307986truemissingunknown
2502false3falsetruehs59falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2512false1falsetruehs6truefalsefalsefalseleast_squaresgeneral-InfInfmissingmissingunknown
2523false1falsetruehs60truefalsetruefalseothergeneral-InfInfmissingmissingunknown
2533false2falsetruehs61truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2543false1falsetruehs62truefalsetruefalseotherlinear-InfInfmissingmissingunknown
2553false2falsetruehs63truefalsetruefalseothergeneral-InfInfmissingmissingunknown
2563false1falsetruehs64falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2573false1falsetruehs65falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2583false2falsetruehs66falsetruetruefalseothergeneral-Inf0.58truemissingunknown
2594false2falsetruehs68truefalsetruefalseothergeneral-InfInftruemissingunknown
2604false2falsetruehs69truefalsetruefalseothergeneral-InfInftruemissingunknown
2612false1falsetruehs7truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2624false1falsetruehs70falsetruetruefalseothergeneral-Inf0.987859truemissingunknown
2634false2falsetruehs71falsefalsetruefalseothergeneral-InfInfmissingmissingunknown
2644false2falsetruehs72falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2654false3falsetruehs73falsefalsetruefalseothergeneral-InfInfmissingmissingunknown
2664false4falsetruehs74falsefalsetruefalseothergeneral-InfInfmissingmissingunknown
2674false4falsetruehs75falsefalsetruefalseothergeneral-InfInfmissingmissingunknown
2684false3falsetruehs76falsetruetruefalseotherlinear-Inf-1.25truemissingunknown
2695false2falsetruehs77truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2705false3falsetruehs78truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2715false3falsetruehs79truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2722false2falsetruehs8truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2735false3falsetruehs80truefalsetruefalseothergeneral-InfInfmissingmissingunknown
2745false3falsetruehs81truefalsetruefalseothergeneral-InfInfmissingmissingunknown
2755false3falsetruehs83falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2765false3falsetruehs84falsetruetruefalseothergeneral-Inf-2.35124e6truemissingunknown
2775false10falsetruehs86falsetruetruefalseotherlinear-Inf20.0truemissingunknown
2786false4falsetruehs87truefalsetruefalseothergeneral-InfInftruemissingunknown
2792false1falsetruehs9truefalsefalsefalseotherlinear-Inf0.0truemissingunknown
2806false2falsetruehs93falsetruetruefalseothergeneral-Inf137.066truemissingunknown
2816false4falsetruehs95falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2826false4falsetruehs96falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2836false4falsetruehs97falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2846false4falsetruehs98falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2857false2falsetruehs99truefalsetruefalseothergeneral-InfInfmissingmissingunknown
286100true0falsetrueindef_modfalsefalsefalsefalseotherunconstrained-Inf91.6655truemissingunknown
287100true0falsetrueintegreqfalsefalsefalsefalseleast_squaresunconstrained-Inf0.0truemissingunknown
2882false0falsetruejennrichsampsonfalsefalsefalsefalseleast_squaresunconstrained-Inf4171.31truemissingunknown
2895false0falsetruekirby2falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
2904false0falsetruekowosbfalsefalsefalsefalseleast_squaresunconstrained-Inf0.0264978truemissingunknown
2916false0falsetruelanczos1falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
2926false0falsetruelanczos2falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
2936false0falsetruelanczos3falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
294100true0falsetrueliarwhdfalsefalsefalsefalseotherunconstrained-Inf58500.0truemissingunknown
29515false11falsetruelinconfalsefalsefalsefalseotherlinear-InfInfmissingmissingunknown
2962false2falsetruelinsvfalsetruefalsefalseotherlinear-InfInfmissingmissingunknown
297111true40truetruemarinetruefalsetruefalseothergeneral-InfInfmissingmissingunknown
2983false0falsetruemeyer3falsefalsefalsefalseleast_squaresunconstrained-Inf1.69361e9truemissingunknown
2992false2falsetruemgh01feastruefalsefalsefalseothergeneral-InfInfmissingmissingunknown
3004false0falsetruemgh09falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3013false0falsetruemgh10falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3025false0falsetruemgh17falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3032false0falsetruemisra1afalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3042false0falsetruemisra1bfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3052false0falsetruemisra1cfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3062false0falsetruemisra1dfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
307100true0falsetruemorebvfalsefalsefalsefalseleast_squaresunconstrained-Inf0.500942truemissingunknown
3082false0falsetruenastyfalsefalsefalsefalseotherunconstrained-Inf0.5truemissingunknown
309100true0falsetruencb20falsefalsefalsefalseotherunconstrained-Inf182.002truemissingunknown
310100true0falsetruencb20bfalsefalsefalsefalseotherunconstrained-Inf200.0truemissingunknown
3113false0falsetruenelsonfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
312100true0falsetruenoncvxu2falsefalsefalsefalseotherunconstrained-Inf2.63975e6truemissingunknown
313100true0falsetruenoncvxunfalsefalsefalsefalseotherunconstrained-Inf2.72701e6truemissingunknown
314100true0falsetruenondiafalsefalsefalsefalseotherunconstrained-Inf39604.0truemissingunknown
315100true0falsetruenondquarfalsefalsefalsefalseotherunconstrained-Inf106.0truemissingunknown
3165false0falsetrueosborne1falsefalsefalsefalseleast_squaresunconstrained-Inf7.06876truemissingunknown
31711false0falsetrueosborne2falsefalsefalsefalseleast_squaresunconstrained-Inf2.09342truemissingunknown
3188false0falsetruepalmer1cfalsefalsefalsefalseleast_squaresunconstrained-Inf3.45295e8truemissingunknown
3197false0falsetruepalmer1dfalsefalsefalsefalseleast_squaresunconstrained-Inf2.87266e7truemissingunknown
3208false0falsetruepalmer2cfalsefalsefalsefalseleast_squaresunconstrained-Inf2.6894e7truemissingunknown
3218false0falsetruepalmer3cfalsefalsefalsefalseleast_squaresunconstrained-Inf8.12197e6truemissingunknown
3228false0falsetruepalmer4cfalsefalsefalsefalseleast_squaresunconstrained-Inf8.09445e6truemissingunknown
3236false0falsetruepalmer5cfalsefalsefalsefalseleast_squaresunconstrained-Inf25495.0truemissingunknown
3244false0falsetruepalmer5dfalsefalsefalsefalseleast_squaresunconstrained-Inf22262.6truemissingunknown
3258false0falsetruepalmer6cfalsefalsefalsefalseleast_squaresunconstrained-Inf7.72166e5truemissingunknown
3268false0falsetruepalmer7cfalsefalsefalsefalseleast_squaresunconstrained-Inf3.20513e6truemissingunknown
3278false0falsetruepalmer8cfalsefalsefalsefalseleast_squaresunconstrained-Inf850271.0truemissingunknown
328100true0falsetruepenalty1falsefalsefalsefalseleast_squaresunconstrained-Inf1.0truemissingunknown
329100true0falsetruepenalty2falsefalsefalsefalseotherunconstrained-Inf1.68848e6truemissingunknown
330100true0falsetruepenalty3falsefalsefalsefalseotherunconstrained-Inf1.00639e8truemissingunknown
331100true50truetruepolygonfalsefalsetruefalseotherlinear-InfInffalsemissingunknown
332100true50truetruepolygon1falsefalsetruefalseotherlinear-InfInffalsemissingunknown
333100true1falsetruepolygon2truefalsetruefalseotherlinear-InfInffalsemissingunknown
334100true100truetruepolygon3falsetruefalsefalseothergeneral-Inf-0.0truemissingunknown
3352false0falsetruepowellbsfalsefalsefalsefalseotherunconstrained-Inf0.567631truemissingunknown
336100true0falsetruepowellsgfalsefalsefalsefalseotherunconstrained-Inf5375.0truemissingunknown
337100true0falsetruepowerfalsefalsefalsefalseleast_squaresunconstrained-Inf2.55025e7truemissingunknown
338100true0falsetruequartcfalsefalsefalsefalseotherunconstrained-Inf1.85427e9truemissingunknown
3393false0falsetruerat42falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3404false0falsetruerat43falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
341109true102truetruerobotarmfalsefalsetruetrueothergeneral-InfInfmissingmissingunknown
3422false0falsetruerosenbrockfalsefalsefalsefalseotherunconstrained-Inf32.3086truemissingunknown
3434false0falsetruerozman1falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
344100true0falsetruesbrybndfalsefalsefalsefalseleast_squaresunconstrained-Inf1568.0truemissingunknown
345100true0falsetrueschmvettfalsefalsefalsefalseotherunconstrained-Inf-189.068truemissingunknown
346100true0falsetruescosinefalsefalsefalsefalseotherunconstrained-Inf86.8807truemissingunknown
347100true0falsetruesinquadfalsefalsefalsefalseotherunconstrained-Inf0.6561truemissingunknown
348100true0falsetruesparsinefalsefalsefalsefalseotherunconstrained-Inf20893.3truemissingunknown
349100true0falsetruesparsqurfalsefalsefalsefalseotherunconstrained-Inf1420.31truemissingunknown
350100true0falsetruespmsrtlsfalsefalsefalsefalseleast_squaresunconstrained-Inf49.3239truemissingunknown
351100true0falsetruesrosenbrfalsefalsefalsefalseotherunconstrained-Inf1210.0truemissingunknown
352600true44truetruestructuralfalsefalsetruefalseotherlinear-InfInfmissingmissingunknown
35315false4falsetruetetrafalsetruetruetrueothergeneral-InfInfmissingmissingunknown
35412597false19222falsetruetetra_duct12falsetruetruetrueothergeneral-Inf23246.1truemissingunknown
3556417false9000falsetruetetra_duct15falsetruetruetrueothergeneral-Inf10890.9truemissingunknown
3563201false4104falsetruetetra_duct20falsetruetruetrueothergeneral-Inf4959.8truemissingunknown
3574011false4847falsetruetetra_foam5falsetruetruetrueothergeneral-Inf6497.1truemissingunknown
3582598false3116falsetruetetra_gearfalsetruetruetrueothergeneral-Inf4256.38truemissingunknown
3593570false4675falsetruetetra_hookfalsetruetruetrueothergeneral-Inf6157.14truemissingunknown
36030false0falsetruethreepkfalsefalsetruefalseotherunconstrained-Inf20236.5truemissingunknown
3617false0falsetruethurberfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
362100true0falsetruetointgssfalsefalsefalsefalseotherunconstrained-Inf891.608truemissingunknown
363100true0falsetruetquarticfalsefalsefalsefalseleast_squaresunconstrained-Inf0.81truemissingunknown
3648false3falsetruetrianglefalsetruetruetrueothergeneral-Inf11.328truemissingunknown
3652244false1896falsetruetriangle_deerfalsetruetruetrueothergeneral-Inf2014.34truemissingunknown
3661366false1182falsetruetriangle_pacmanfalsetruetruetrueothergeneral-Inf1316.28truemissingunknown
3674444false4025falsetruetriangle_turtlefalsetruetruetrueothergeneral-Inf4467.58truemissingunknown
368100true0falsetruetridiafalsefalsefalsefalseotherunconstrained-Inf5049.0truemissingunknown
369100true0falsetruevardimfalsefalsefalsefalseotherunconstrained-Inf1.31058e14truemissingunknown
3708false0falsetruevibrbeamfalsefalsefalsefalseleast_squaresunconstrained-Inf8231.28truemissingunknown
37131false0falsetruewatsonfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
372100true0falsetruewoodsfalsefalsefalsefalseotherunconstrained-Inf180451.0truemissingunknown
3733false3falsetruezangwil3truefalsefalsefalseotherlinear-InfInfmissingmissingunknown

Then, it is very simple to filter problems using queries on DataFrame. We refer to the documentation of DataFrames.jl for tutorials. For instance, if one wants to select unconstrained scalable problems and use (:nvar, :name).

meta = OptimizationProblems.meta
+  └ :x

Global meta

This package collects all the metadata in a single DataFrame.

OptimizationProblems.meta
374×17 DataFrame
Rownvarvariable_nvarnconvariable_nconminimizenamehas_equalities_onlyhas_inequalities_onlyhas_boundshas_fixed_variablesobjtypecontypebest_known_lower_boundbest_known_upper_boundis_feasibledefined_everywhereorigin
Int64BoolInt64BoolBoolStringBoolBoolBoolBoolSymbolSymbolRealRealBool?Bool?Symbol
11false0falsetrueAMPGO02falsefalsefalsefalseotherunconstrained-Inf0.839498truemissingunknown
21false0falsetrueAMPGO03falsefalsefalsefalseotherunconstrained-Inf2.88961truemissingunknown
31false0falsetrueAMPGO04falsefalsefalsefalseotherunconstrained-Inf-2.5666truemissingunknown
41false0falsetrueAMPGO05falsefalsefalsefalseotherunconstrained-Inf-0.0truemissingunknown
51false0falsetrueAMPGO06falsefalsefalsefalseotherunconstrained-Inf3.5177e-43truemissingunknown
61false0falsetrueAMPGO07falsefalsefalsefalseotherunconstrained-Inf2.56475truemissingunknown
71false0falsetrueAMPGO08falsefalsefalsefalseotherunconstrained-Inf-2.0928truemissingunknown
81false0falsetrueAMPGO09falsefalsefalsefalseotherunconstrained-Inf0.921136truemissingunknown
91false0falsetrueAMPGO10falsefalsefalsefalseotherunconstrained-Inf-0.0truemissingunknown
101false0falsetrueAMPGO11falsefalsefalsefalseotherunconstrained-Inf-1.0truemissingunknown
111false0falsetrueAMPGO12falsefalsefalsefalseotherunconstrained-Inf1.0truemissingunknown
121false0falsetrueAMPGO13falsefalsefalsefalseotherunconstrained-1.5874-1.5874truemissingunknown
131false0falsetrueAMPGO14falsefalsefalsefalseotherunconstrained-Inf-0.0truemissingunknown
141false0falsetrueAMPGO15falsefalsefalsefalseotherunconstrained-Inf2.15385truemissingunknown
151false0falsetrueAMPGO18falsefalsefalsefalseotherunconstrained-Inf4.0truemissingunknown
161false0falsetrueAMPGO20falsefalsefalsefalseotherunconstrained-Inf3.92246e-43truemissingunknown
171false0falsetrueAMPGO21falsefalsefalsefalseotherunconstrained-Inf0.0truemissingunknown
181false0falsetrueAMPGO22falsefalsefalsefalseotherunconstrained-Inf1.0truemissingunknown
193false1falsetrueBOX2truefalsefalsefalseotherlinear-Inf0.942284truemissingunknown
203false0falsetrueBOX3falsefalsefalsefalseotherunconstrained-Inf662.868truemissingunknown
211false0falsetrueDus2_1falsefalsefalsefalseotherunconstrained-Inf7.38906truemissingunknown
221false0falsetrueDus2_3falsefalsefalsefalseotherunconstrained-Inf0.972973truemissingunknown
231false0falsetrueDus2_9falsefalsefalsefalseotherunconstrained-Inf1.0truemissingunknown
241false0falsetrueDuscubefalsefalsefalsefalseotherunconstrained-Inf-108.0truemissingunknown
2591true0falsetrueNZF1falsefalsefalsefalseleast_squaresunconstrained-Inf34698.4truemissingunknown
261false0falsetrueShpak1falsefalsefalsefalseotherunconstrained-Inf0.839498truemissingunknown
271false0falsetrueShpak2falsefalsefalsefalseotherunconstrained-Inf-0.747036truemissingunknown
281false0falsetrueShpak3falsefalsefalsefalseotherunconstrained-Inf2.63055truemissingunknown
291false0falsetrueShpak4falsefalsefalsefalseotherunconstrained-Inf-0.677439truemissingunknown
301false0falsetrueShpak5falsefalsefalsefalseotherunconstrained-Inf-1.00842truemissingunknown
311false0falsetrueShpak6falsefalsefalsefalseotherunconstrained-Inf-1.25667truemissingunknown
328false5falsetrueaircrftatruefalsetruetrueothergeneral-Inf0truemissingunknown
334false3falsetrueallinitfalsefalsefalsefalseotherlinear-InfInfmissingmissingunknown
344false4falsetrueallinitcfalsefalsefalsefalseothergeneral-InfInfmissingmissingunknown
354false0falsetrueallinitufalsefalsefalsefalseotherunconstrained-Inf13.0truemissingunknown
362false3falsetruealsotamefalsefalsefalsefalseothergeneral-InfInfmissingmissingunknown
373false15falsetrueargausstruefalsefalsefalseothergeneral-InfInfmissingmissingunknown
38100true0falsetruearglinafalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
39100true0falsetruearglinbfalsefalsefalsefalseleast_squaresunconstrained-Inf6.85174e13truemissingunknown
40100true0falsetruearglincfalsefalsefalsefalseotherunconstrained-Inf6.38544e13truemissingunknown
41100true0falsetrueargtrigfalsefalsefalsefalseotherunconstrained-Inf0.0truemissingunknown
42100true0falsetruearwheadfalsefalsefalsefalseotherunconstrained-Inf297.0truemissingunknown
4349false15falsetrueavion2truefalsetruefalseleast_squareslinear-InfInfmissingmissingunknown
443false0falsetruebardfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
45100true0falsetruebdqrticfalsefalsefalsefalseleast_squaresunconstrained-Inf21696.0truemissingunknown
462false0falsetruebealefalsefalsefalsefalseleast_squaresunconstrained-Inf14.2031truemissingunknown
47100true0falsetruebearingfalsefalsetruetrueotherunconstrained-Inf15.0662truemissingunknown
483false0falsetruebennett5falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
496false0falsetruebiggs5falsefalsetruetrueotherunconstrained-Inf-3.15621truemissingunknown
506false0falsetruebiggs6falsefalsefalsefalseotherunconstrained-Inf-3.15621truemissingunknown
512false2falsetrueboothtruefalsefalsefalseotherlinear-InfInfmissingmissingunknown
522false0falsetrueboxbodfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
531false1falsetruebqp1varfalsetruefalsefalseotherlinear-Inf0.3125truemissingunknown
54450false360falsetruebritgastruefalsetruefalseothergeneral-InfInfmissingmissingunknown
55100true0falsetruebrownalfalsefalsefalsefalseleast_squaresunconstrained-Inf0.0truemissingunknown
562false0falsetruebrownbsfalsefalsefalsefalseleast_squaresunconstrained-Inf9.99998e11truemissingunknown
574false0falsetruebrowndenfalsefalsefalsefalseleast_squaresunconstrained-Inf7.92669e6truemissingunknown
58100true0falsetruebroyden3dfalsefalsefalsefalseleast_squaresunconstrained-Inf0.0truemissingunknown
59100true0falsetruebroydn7dfalsefalsefalsefalseotherunconstrained-Inf274.204truemissingunknown
60100true0falsetruebrybndfalsefalsefalsefalseleast_squaresunconstrained-Inf3600.0truemissingunknown
612false1falsetruebt1truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
62100true203truetruecamshapefalsetruetruefalseotherquadratic-InfInfmissingmissingunknown
6399true32truetruecatenarytruefalsetruetruelinearquadratic-InfInfmissingmissingacademic
64100true77truetruechaintruefalsefalsefalseothergeneral-InfInfmissingmissingunknown
65100true0falsetruechainwoofalsefalsefalsefalseotherunconstrained-Inf3.71954e5truemissingunknown
6696true96truetruechanneltruefalsefalsefalseothergeneral-InfInfmissingmissingunknown
67100true0falsetruechnrosnb_modfalsefalsefalsefalseotherunconstrained-Inf17637.9truemissingunknown
683false0falsetruechwirut1falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
693false0falsetruechwirut2falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
702false0falsetrueclifffalsefalsefalsefalseotherunconstrained-Inf4.85165e8truemissingunknown
7199true64truetrueclnlbeamtruefalsetruefalseothergeneral-Inf350.0truemissingunknown
72100true0falsetrueclplateafalsefalsefalsefalseotherunconstrained-Inf0.0truemissingunknown
73100true0falsetrueclplatebfalsefalsefalsefalseotherunconstrained-Inf0.0truemissingunknown
74100true0falsetrueclplatecfalsefalsefalsefalseotherunconstrained-Inf0.0truemissingunknown
75100true50truetruecontrolinvestmenttruefalsetruefalseothergeneral-Inf-0.98truemissingunknown
76100true0falsetruecosinefalsefalsefalsefalseotherunconstrained-Inf86.8807truemissingunknown
77100true0falsetruecragglvyfalsefalsefalsefalseotherunconstrained-Inf52823.1truemissingunknown
78100true0falsetruecragglvy2falsefalsefalsefalseotherunconstrained-Inf52823.1truemissingunknown
79100true0falsetruecurlyfalsefalsefalsefalseotherunconstrained-Inf-0.00623722truemissingunknown
80100true0falsetruecurly10falsefalsefalsefalseotherunconstrained-Inf-0.00623722truemissingunknown
81100true0falsetruecurly20falsefalsefalsefalseotherunconstrained-Inf-0.0129654truemissingunknown
82100true0falsetruecurly30falsefalsefalsefalseotherunconstrained-Inf-0.020383truemissingunknown
832false0falsetruedanwoodfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
8499true0falsetruedixmaanefalsefalsefalsefalseotherunconstrained-Inf731.833truemissingunknown
8599true0falsetruedixmaanffalsefalsefalsefalseotherunconstrained-Inf1348.42truemissingunknown
8699true0falsetruedixmaangfalsefalsefalsefalseotherunconstrained-Inf2495.83truemissingunknown
8799true0falsetruedixmaanhfalsefalsefalsefalseotherunconstrained-Inf4974.25truemissingunknown
8899true0falsetruedixmaanifalsefalsefalsefalseotherunconstrained-Inf663.646truemissingunknown
8999true0falsetruedixmaanjfalsefalsefalsefalseotherunconstrained-Inf1281.33truemissingunknown
9099true0falsetruedixmaankfalsefalsefalsefalseotherunconstrained-Inf2427.65truemissingunknown
9199true0falsetruedixmaanlfalsefalsefalsefalseotherunconstrained-Inf4903.7truemissingunknown
9299true0falsetruedixmaanmfalsefalsefalsefalseotherunconstrained-Inf314.313truemissingunknown
9399true0falsetruedixmaannfalsefalsefalsefalseotherunconstrained-Inf665.66truemissingunknown
9499true0falsetruedixmaanofalsefalsefalsefalseotherunconstrained-Inf1196.31truemissingunknown
9599true0falsetruedixmaanpfalsefalsefalsefalseotherunconstrained-Inf2342.52truemissingunknown
96100true0falsetruedixon3dqfalsefalsefalsefalseleast_squaresunconstrained-Inf8.0truemissingunknown
97100true0falsetruedqdrticfalsefalsefalsefalseotherunconstrained-Inf177282.0truemissingunknown
98100true0falsetruedqrticfalsefalsefalsefalseotherunconstrained-Inf1.85427e9truemissingunknown
993false0falsetrueeckerle4falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
100100true0falsetrueedenschfalsefalsefalsefalseotherunconstrained-Inf1699.0truemissingunknown
101100true0falsetrueeg2falsefalsefalsefalseotherunconstrained-Inf-83.3056truemissingunknown
10299true33truetrueelectruefalsefalsefalseotherquadratic-InfInfmissingmissingunknown
103100true0falsetrueengval1falsefalsefalsefalseotherunconstrained-Inf5841.0truemissingunknown
1049false0falsetrueensofalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
105100true0falsetrueerrinros_modfalsefalsefalsefalseleast_squaresunconstrained-Inf3.13991e5truemissingunknown
106100true0falsetrueextrosnbfalsefalsefalsefalseotherunconstrained-Inf39604.0truemissingunknown
107100true0falsetruefletcbv2falsefalsefalsefalseotherunconstrained-Inf-0.513108truemissingunknown
108100true0falsetruefletcbv3_modfalsefalsefalsefalseotherunconstrained-Inf-0.0187925truemissingunknown
109100true0falsetruefletchcrfalsefalsefalsefalseotherunconstrained-Inf9900.0truemissingunknown
110100false0falsetruefminsrf2falsefalsefalsefalseotherunconstrained-Inf2504.27truemissingunknown
111100true0falsetruefreurothfalsefalsefalsefalseleast_squaresunconstrained-Inf99556.5truemissingunknown
1128false0falsetruegauss1falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
1138false0falsetruegauss2falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
1148false0falsetruegauss3falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
1153false0falsetruegaussianfalsefalsefalsefalseleast_squaresunconstrained-Inf3.88811e-6truemissingunknown
116100true0falsetruegenhumpsfalsefalsefalsefalseotherunconstrained-Inf2.53684e6truemissingunknown
117100true0falsetruegenrosefalsefalsefalsefalseotherunconstrained-Inf405.106truemissingunknown
118100true0falsetruegenrose_nashfalsefalsefalsefalseotherunconstrained-Inf404.126truemissingunknown
1193false0falsetruegulffalsefalsefalsefalseleast_squaresunconstrained-Inf0.0truemissingunknown
1207false0falsetruehahn1falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
1213false0falsetruehelicalfalsefalsefalsefalseotherunconstrained-Inf0.0truemissingunknown
12298true68truetruehovercraft1dtruefalsefalsefalseleast_squareslinear-InfInfmissingmissingunknown
1232false0falsetruehs1falsefalsetruefalseotherunconstrained-Inf909.0truemissingunknown
1242false1falsetruehs10falsetruefalsefalseothergeneral-InfInfmissingmissingunknown
1257false4falsetruehs100falsetruefalsefalseothergeneral-Inf714.0truemissingunknown
1267false5falsetruehs101falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1277false5falsetruehs102falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1287false5falsetruehs103falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1298false5falsetruehs104falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1308false1falsetruehs105falsetruetruefalseotherlinear-InfInfmissingmissingunknown
1318false6falsetruehs106falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1329false6falsetruehs107truefalsetruefalseothergeneral-InfInfmissingmissingunknown
1339false12falsetruehs108falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1349false9falsetruehs109falsefalsetruefalseothergeneral-InfInfmissingmissingunknown
1352false1falsetruehs11falsetruefalsefalseothergeneral-InfInfmissingmissingunknown
13610false0falsetruehs110falsefalsetruefalseotherunconstrained-Inf-43.1343truemissingunknown
13710false3falsetruehs111truefalsetruefalseothergeneral-InfInfmissingmissingunknown
13810false3falsetruehs112truefalsetruefalseotherlinear-InfInfmissingmissingunknown
13910false8falsetruehs113falsetruefalsefalseothergeneral-Inf753.0truemissingunknown
14010false11falsetruehs114falsefalsetruefalseothergeneral-InfInfmissingmissingunknown
14113false15falsetruehs116falsetruetruefalseothergeneral-InfInfmissingmissingunknown
14215false5falsetruehs117falsetruetruefalseothergeneral-Inf2400.11truemissingunknown
14315false17falsetruehs118falsetruetruefalseotherlinear-Inf942.716truemissingunknown
14416false8falsetruehs119truefalsetruefalseotherlinear-InfInfmissingmissingunknown
1452false1falsetruehs12falsetruefalsefalseothergeneral-Inf0.0truemissingunknown
1462false1falsetruehs13falsetruetruefalseleast_squaresgeneral-InfInfmissingmissingunknown
1472false2falsetruehs14falsefalsefalsefalseleast_squaresgeneral-InfInfmissingmissingunknown
1482false2falsetruehs15falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1492false2falsetruehs16falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1502false2falsetruehs17falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1512false2falsetruehs18falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1522false2falsetruehs19falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1532false0falsetruehs2falsefalsetruefalseotherunconstrained-Inf909.0truemissingunknown
1542false3falsetruehs20falsetruetruefalseothergeneral-InfInfmissingmissingunknown
1552false0falsetruehs201falsefalsefalsefalseotherunconstrained-Inf45.0truemissingunknown
1562false1falsetruehs21falsetruetruefalseotherlinear-InfInfmissingmissingunknown
1572false0falsetruehs211falsefalsefalsefalseotherunconstrained-Inf750.0truemissingunknown
1584false2falsetruehs219truefalsefalsefalselineargeneral-1.0-1.0truetrueunknown
1592false2falsetruehs22falsetruefalsefalseleast_squaresgeneral-InfInfmissingmissingunknown
1602false1falsetruehs220truefalsetruefalseothergeneral125000truemissingunknown
1612false1falsetruehs221falsetruetruefalseothergeneral-1-0.25truemissingunknown
1622false1falsetruehs222falsetruetruefalseothergeneral-1.5-1.3truemissingunknown
1632false2falsetruehs223falsetruetruefalseothergeneral-0.834032-0.1truemissingunknown
1642false4falsetruehs224falsetruetruefalseotherlinear-304-304truemissingunknown
1652false5falsetruehs225falsetruefalsefalseothergeneral22truemissingunknown
1662false2falsetruehs226falsetruetruefalseothergeneral-0.5-0.5truemissingunknown
1672false2falsetruehs227falsetruefalsefalseothergeneral11truemissingunknown
1682false2falsetruehs228falsetruefalsefalseothergeneral-3-3truemissingunknown
1692false0falsetruehs229falsefalsetruefalseotherunconstrained00truemissingunknown
1702false5falsetruehs23falsetruetruefalseleast_squaresgeneral-InfInfmissingmissingunknown
1712false2falsetruehs230falsetruefalsefalseothergeneral0.3750.375truemissingunknown
1722false2falsetruehs231falsetruefalsefalseotherlinear00truemissingunknown
1732false3falsetruehs232falsetruetruefalseotherlinear-1-1truemissingunknown
1742false1falsetruehs233falsetruefalsefalseotherunconstrained00truemissingunknown
1752false1falsetruehs234falsetruetruefalseothergeneral-0.8-0.8truemissingunknown
1763false1falsetruehs235truefalsefalsefalseothergeneral0.040.04truemissingunknown
1772false2falsetruehs236falsetruetruefalseothergeneral-58.9034-58.9034truemissingunknown
1782false3falsetruehs237falsetruetruefalseothergeneral-58.9034-58.9034truemissingunknown
1792false3falsetruehs238falsetruetruefalseothergeneral-58.9034-58.9034truemissingunknown
1802false1falsetruehs239falsetruetruefalseothergeneral-58.9034-58.9034truemissingunknown
1812false2falsetruehs24falsetruetruefalseotherlinear-Inf-0.0133646truemissingunknown
1823false0falsetruehs240falsefalsefalsefalseotherunconstrained00truemissingunknown
1833false0falsetruehs241falsefalsefalsefalseotherunconstrained00truemissingunknown
1843false0falsetruehs242falsefalsetruefalseotherunconstrained00truemissingunknown
1853false0falsetruehs243falsefalsefalsefalseotherunconstrained0.79660.7966truemissingunknown
1863false0falsetruehs244falsefalsefalsefalseotherunconstrained00truemissingunknown
1873false0falsetruehs245falsefalsefalsefalseotherunconstrained00truemissingunknown
1883false0falsetruehs246falsefalsefalsefalseotherunconstrained00truemissingunknown
1893false2falsetruehs248falsefalsefalsefalseothergeneral-0.8-0.8truemissingunknown
1903false1falsetruehs249falsetruetruefalseothergeneral11truemissingunknown
1913false0falsetruehs25falsefalsetruefalseotherunconstrained-Inf32.835truemissingunknown
1923false2falsetruehs250falsetruetruefalseotherlinear-3300-3300truemissingunknown
1933false1falsetruehs251falsetruetruefalseotherlinear-3456-3456truemissingunknown
1943false1falsetruehs252truefalsefalsefalseotherunconstrained0.040.04truemissingunknown
1953false1falsetruehs253falsetruetruefalseotherlinear87.379487.3794truemissingunknown
1963false2falsetruehs254truefalsetruefalseothergeneral-1.73205-1.73205truemissingunknown
1974false0falsetruehs255falsefalsefalsefalseotherunconstrained00truemissingunknown
1984false0falsetruehs256falsefalsefalsefalseotherunconstrained00truemissingunknown
1994false0falsetruehs257falsefalsetruefalseotherunconstrained00truemissingunknown
2004false0falsetruehs258falsefalsefalsefalseotherunconstrained00truemissingunknown
2014false0falsetruehs259falsefalsefalsefalseotherunconstrained00truemissingunknown
2023false1falsetruehs26truefalsefalsefalseothergeneral-Inf21.16truemissingunknown
2034false0falsetruehs260falsefalsefalsefalseotherunconstrained00truemissingunknown
2044false0falsetruehs261falsefalsefalsefalseotherunconstrained00truemissingunknown
2054false4falsetruehs262falsefalsetruefalseotherlinear-10-10truemissingunknown
2064false4falsetruehs263falsefalsefalsefalseothergeneral-1-1truemissingunknown
2074false3falsetruehs264falsetruefalsefalseothergeneral-44-44truemissingunknown
2084false2falsetruehs265truefalsetruefalseotherlinear0.9747470.974747truemissingunknown
2093false1falsetruehs27truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2103false1falsetruehs28truefalsefalsefalseleast_squareslinear-Inf13.0truemissingunknown
2113false1falsetruehs29falsetruefalsefalseothergeneral-Inf-1.0truemissingunknown
2122false0falsetruehs3falsefalsetruefalseotherunconstrained-Inf1.00081truemissingunknown
2133false1falsetruehs30falsetruetruefalseleast_squaresgeneral-Inf3.0truemissingunknown
2143false1falsetruehs31falsetruetruefalseothergeneral-Inf19.0truemissingunknown
2152false1falsetruehs316truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2162false1falsetruehs317truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2172false1falsetruehs318truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2182false1falsetruehs319truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2193false2falsetruehs32falsefalsetruefalseothergeneral-Inf7.2truemissingunknown
2202false1falsetruehs320truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2212false1falsetruehs321truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2222false1falsetruehs322truefalsefalsefalsequadraticquadratic-InfInftruemissingacademic
2233false2falsetruehs33falsetruetruefalseothergeneral-Inf-3.0truemissingunknown
2243false2falsetruehs34falsetruetruefalseothergeneral-Inf0.0truemissingunknown
2253false1falsetruehs35falsetruetruefalseotherlinear-Inf2.25truemissingunknown
2263false1falsetruehs36falsetruetruefalseotherlinear-Inf-1000.0truemissingunknown
2273false1falsetruehs37falsetruetruefalseotherlinear-Inf-1000.0truemissingunknown
22810false3falsetruehs378truefalsefalsefalseothergeneral-InfInftruemissingacademic
2294false0falsetruehs38falsefalsetruefalseotherunconstrained-Inf19192.0truemissingunknown
2304false2falsetruehs39truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2312false0falsetruehs4falsefalsetruefalseotherunconstrained-Inf3.32357truemissingunknown
2324false3falsetruehs40truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2334false1falsetruehs41truefalsetruefalseotherlinear-InfInfmissingmissingunknown
2344false2falsetruehs42truefalsefalsefalseleast_squaresgeneral-InfInfmissingmissingunknown
2354false3falsetruehs43falsetruefalsefalseothergeneral-Inf0.0truemissingunknown
2364false6falsetruehs44falsetruetruefalseotherlinear-Inf0.0truemissingunknown
2375false0falsetruehs45falsefalsetruefalseotherunconstrained-Inf1.73333truemissingunknown
2385false2falsetruehs46truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2395false3falsetruehs47truefalsefalsefalseothergeneral-Inf20.7381truemissingunknown
2405false2falsetruehs48truefalsefalsefalseleast_squareslinear-Inf84.0truemissingunknown
2415false2falsetruehs49truefalsefalsefalseotherlinear-Inf266.0truemissingunknown
2422false0falsetruehs5falsefalsetruefalseotherunconstrained-Inf1.0truemissingunknown
2435false3falsetruehs50truefalsefalsefalseotherlinear-Inf7516.0truemissingunknown
2445false3falsetruehs51truefalsefalsefalseleast_squareslinear-Inf8.5truemissingunknown
2455false3falsetruehs52truefalsefalsefalseleast_squareslinear-InfInfmissingmissingunknown
2465false3falsetruehs53truefalsetruefalseleast_squareslinear-InfInfmissingmissingunknown
2476false1falsetruehs54truefalsetruefalseotherlinear-InfInfmissingmissingunknown
2486false6falsetruehs55truefalsetruefalseotherlinear-InfInfmissingmissingunknown
2497false4falsetruehs56truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2502false1falsetruehs57falsetruetruefalseleast_squaresgeneral-Inf0.0307986truemissingunknown
2512false3falsetruehs59falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2522false1falsetruehs6truefalsefalsefalseleast_squaresgeneral-InfInfmissingmissingunknown
2533false1falsetruehs60truefalsetruefalseothergeneral-InfInfmissingmissingunknown
2543false2falsetruehs61truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2553false1falsetruehs62truefalsetruefalseotherlinear-InfInfmissingmissingunknown
2563false2falsetruehs63truefalsetruefalseothergeneral-InfInfmissingmissingunknown
2573false1falsetruehs64falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2583false1falsetruehs65falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2593false2falsetruehs66falsetruetruefalseothergeneral-Inf0.58truemissingunknown
2604false2falsetruehs68truefalsetruefalseothergeneral-InfInftruemissingunknown
2614false2falsetruehs69truefalsetruefalseothergeneral-InfInftruemissingunknown
2622false1falsetruehs7truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2634false1falsetruehs70falsetruetruefalseothergeneral-Inf0.987859truemissingunknown
2644false2falsetruehs71falsefalsetruefalseothergeneral-InfInfmissingmissingunknown
2654false2falsetruehs72falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2664false3falsetruehs73falsefalsetruefalseothergeneral-InfInfmissingmissingunknown
2674false4falsetruehs74falsefalsetruefalseothergeneral-InfInfmissingmissingunknown
2684false4falsetruehs75falsefalsetruefalseothergeneral-InfInfmissingmissingunknown
2694false3falsetruehs76falsetruetruefalseotherlinear-Inf-1.25truemissingunknown
2705false2falsetruehs77truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2715false3falsetruehs78truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2725false3falsetruehs79truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2732false2falsetruehs8truefalsefalsefalseothergeneral-InfInfmissingmissingunknown
2745false3falsetruehs80truefalsetruefalseothergeneral-InfInfmissingmissingunknown
2755false3falsetruehs81truefalsetruefalseothergeneral-InfInfmissingmissingunknown
2765false3falsetruehs83falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2775false3falsetruehs84falsetruetruefalseothergeneral-Inf-2.35124e6truemissingunknown
2785false10falsetruehs86falsetruetruefalseotherlinear-Inf20.0truemissingunknown
2796false4falsetruehs87truefalsetruefalseothergeneral-InfInftruemissingunknown
2802false1falsetruehs9truefalsefalsefalseotherlinear-Inf0.0truemissingunknown
2816false2falsetruehs93falsetruetruefalseothergeneral-Inf137.066truemissingunknown
2826false4falsetruehs95falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2836false4falsetruehs96falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2846false4falsetruehs97falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2856false4falsetruehs98falsetruetruefalseothergeneral-InfInfmissingmissingunknown
2867false2falsetruehs99truefalsetruefalseothergeneral-InfInfmissingmissingunknown
287100true0falsetrueindef_modfalsefalsefalsefalseotherunconstrained-Inf91.6655truemissingunknown
288100true0falsetrueintegreqfalsefalsefalsefalseleast_squaresunconstrained-Inf0.0truemissingunknown
2892false0falsetruejennrichsampsonfalsefalsefalsefalseleast_squaresunconstrained-Inf4171.31truemissingunknown
2905false0falsetruekirby2falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
2914false0falsetruekowosbfalsefalsefalsefalseleast_squaresunconstrained-Inf0.0264978truemissingunknown
2926false0falsetruelanczos1falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
2936false0falsetruelanczos2falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
2946false0falsetruelanczos3falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
295100true0falsetrueliarwhdfalsefalsefalsefalseotherunconstrained-Inf58500.0truemissingunknown
29615false11falsetruelinconfalsefalsefalsefalseotherlinear-InfInfmissingmissingunknown
2972false2falsetruelinsvfalsetruefalsefalseotherlinear-InfInfmissingmissingunknown
298111true40truetruemarinetruefalsetruefalseothergeneral-InfInfmissingmissingunknown
2993false0falsetruemeyer3falsefalsefalsefalseleast_squaresunconstrained-Inf1.69361e9truemissingunknown
3002false2falsetruemgh01feastruefalsefalsefalseothergeneral-InfInfmissingmissingunknown
3014false0falsetruemgh09falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3023false0falsetruemgh10falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3035false0falsetruemgh17falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3042false0falsetruemisra1afalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3052false0falsetruemisra1bfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3062false0falsetruemisra1cfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3072false0falsetruemisra1dfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
308100true0falsetruemorebvfalsefalsefalsefalseleast_squaresunconstrained-Inf0.500942truemissingunknown
3092false0falsetruenastyfalsefalsefalsefalseotherunconstrained-Inf0.5truemissingunknown
310100true0falsetruencb20falsefalsefalsefalseotherunconstrained-Inf182.002truemissingunknown
311100true0falsetruencb20bfalsefalsefalsefalseotherunconstrained-Inf200.0truemissingunknown
3123false0falsetruenelsonfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
313100true0falsetruenoncvxu2falsefalsefalsefalseotherunconstrained-Inf2.63975e6truemissingunknown
314100true0falsetruenoncvxunfalsefalsefalsefalseotherunconstrained-Inf2.72701e6truemissingunknown
315100true0falsetruenondiafalsefalsefalsefalseotherunconstrained-Inf39604.0truemissingunknown
316100true0falsetruenondquarfalsefalsefalsefalseotherunconstrained-Inf106.0truemissingunknown
3175false0falsetrueosborne1falsefalsefalsefalseleast_squaresunconstrained-Inf7.06876truemissingunknown
31811false0falsetrueosborne2falsefalsefalsefalseleast_squaresunconstrained-Inf2.09342truemissingunknown
3198false0falsetruepalmer1cfalsefalsefalsefalseleast_squaresunconstrained-Inf3.45295e8truemissingunknown
3207false0falsetruepalmer1dfalsefalsefalsefalseleast_squaresunconstrained-Inf2.87266e7truemissingunknown
3218false0falsetruepalmer2cfalsefalsefalsefalseleast_squaresunconstrained-Inf2.6894e7truemissingunknown
3228false0falsetruepalmer3cfalsefalsefalsefalseleast_squaresunconstrained-Inf8.12197e6truemissingunknown
3238false0falsetruepalmer4cfalsefalsefalsefalseleast_squaresunconstrained-Inf8.09445e6truemissingunknown
3246false0falsetruepalmer5cfalsefalsefalsefalseleast_squaresunconstrained-Inf25495.0truemissingunknown
3254false0falsetruepalmer5dfalsefalsefalsefalseleast_squaresunconstrained-Inf22262.6truemissingunknown
3268false0falsetruepalmer6cfalsefalsefalsefalseleast_squaresunconstrained-Inf7.72166e5truemissingunknown
3278false0falsetruepalmer7cfalsefalsefalsefalseleast_squaresunconstrained-Inf3.20513e6truemissingunknown
3288false0falsetruepalmer8cfalsefalsefalsefalseleast_squaresunconstrained-Inf850271.0truemissingunknown
329100true0falsetruepenalty1falsefalsefalsefalseleast_squaresunconstrained-Inf1.0truemissingunknown
330100true0falsetruepenalty2falsefalsefalsefalseotherunconstrained-Inf1.68848e6truemissingunknown
331100true0falsetruepenalty3falsefalsefalsefalseotherunconstrained-Inf1.00639e8truemissingunknown
332100true50truetruepolygonfalsefalsetruefalseotherlinear-InfInffalsemissingunknown
333100true50truetruepolygon1falsefalsetruefalseotherlinear-InfInffalsemissingunknown
334100true1falsetruepolygon2truefalsetruefalseotherlinear-InfInffalsemissingunknown
335100true100truetruepolygon3falsetruefalsefalseothergeneral-Inf-0.0truemissingunknown
3362false0falsetruepowellbsfalsefalsefalsefalseotherunconstrained-Inf0.567631truemissingunknown
337100true0falsetruepowellsgfalsefalsefalsefalseotherunconstrained-Inf5375.0truemissingunknown
338100true0falsetruepowerfalsefalsefalsefalseleast_squaresunconstrained-Inf2.55025e7truemissingunknown
339100true0falsetruequartcfalsefalsefalsefalseotherunconstrained-Inf1.85427e9truemissingunknown
3403false0falsetruerat42falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
3414false0falsetruerat43falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
342109true102truetruerobotarmfalsefalsetruetrueothergeneral-InfInfmissingmissingunknown
3432false0falsetruerosenbrockfalsefalsefalsefalseotherunconstrained-Inf32.3086truemissingunknown
3444false0falsetruerozman1falsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
345100true0falsetruesbrybndfalsefalsefalsefalseleast_squaresunconstrained-Inf1568.0truemissingunknown
346100true0falsetrueschmvettfalsefalsefalsefalseotherunconstrained-Inf-189.068truemissingunknown
347100true0falsetruescosinefalsefalsefalsefalseotherunconstrained-Inf86.8807truemissingunknown
348100true0falsetruesinquadfalsefalsefalsefalseotherunconstrained-Inf0.6561truemissingunknown
349100true0falsetruesparsinefalsefalsefalsefalseotherunconstrained-Inf20893.3truemissingunknown
350100true0falsetruesparsqurfalsefalsefalsefalseotherunconstrained-Inf1420.31truemissingunknown
351100true0falsetruespmsrtlsfalsefalsefalsefalseleast_squaresunconstrained-Inf49.3239truemissingunknown
352100true0falsetruesrosenbrfalsefalsefalsefalseotherunconstrained-Inf1210.0truemissingunknown
353600true44truetruestructuralfalsefalsetruefalseotherlinear-InfInfmissingmissingunknown
35415false4falsetruetetrafalsetruetruetrueothergeneral-InfInfmissingmissingunknown
35512597false19222falsetruetetra_duct12falsetruetruetrueothergeneral-Inf23246.1truemissingunknown
3566417false9000falsetruetetra_duct15falsetruetruetrueothergeneral-Inf10890.9truemissingunknown
3573201false4104falsetruetetra_duct20falsetruetruetrueothergeneral-Inf4959.8truemissingunknown
3584011false4847falsetruetetra_foam5falsetruetruetrueothergeneral-Inf6497.1truemissingunknown
3592598false3116falsetruetetra_gearfalsetruetruetrueothergeneral-Inf4256.38truemissingunknown
3603570false4675falsetruetetra_hookfalsetruetruetrueothergeneral-Inf6157.14truemissingunknown
36130false0falsetruethreepkfalsefalsetruefalseotherunconstrained-Inf20236.5truemissingunknown
3627false0falsetruethurberfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
363100true0falsetruetointgssfalsefalsefalsefalseotherunconstrained-Inf891.608truemissingunknown
364100true0falsetruetquarticfalsefalsefalsefalseleast_squaresunconstrained-Inf0.81truemissingunknown
3658false3falsetruetrianglefalsetruetruetrueothergeneral-Inf11.328truemissingunknown
3662244false1896falsetruetriangle_deerfalsetruetruetrueothergeneral-Inf2014.34truemissingunknown
3671366false1182falsetruetriangle_pacmanfalsetruetruetrueothergeneral-Inf1316.28truemissingunknown
3684444false4025falsetruetriangle_turtlefalsetruetruetrueothergeneral-Inf4467.58truemissingunknown
369100true0falsetruetridiafalsefalsefalsefalseotherunconstrained-Inf5049.0truemissingunknown
370100true0falsetruevardimfalsefalsefalsefalseotherunconstrained-Inf1.31058e14truemissingunknown
3718false0falsetruevibrbeamfalsefalsefalsefalseleast_squaresunconstrained-Inf8231.28truemissingunknown
37231false0falsetruewatsonfalsefalsefalsefalseleast_squaresunconstrained-Inf500.0truemissingunknown
373100true0falsetruewoodsfalsefalsefalsefalseotherunconstrained-Inf180451.0truemissingunknown
3743false3falsetruezangwil3truefalsefalsefalseotherlinear-InfInfmissingmissingunknown

Then, it is very simple to filter problems using queries on DataFrame. We refer to the documentation of DataFrames.jl for tutorials. For instance, if one wants to select unconstrained scalable problems and use (:nvar, :name).

meta = OptimizationProblems.meta
 names_pb_vars = meta[(meta.variable_nvar .== true) .& (meta.ncon .== 0), [:nvar, :name]]
80×2 DataFrame
Rownvarname
Int64String
191NZF1
2100arglina
3100arglinb
4100arglinc
5100argtrig
6100arwhead
7100bdqrtic
8100bearing
9100brownal
10100broyden3d
11100broydn7d
12100brybnd
13100chainwoo
14100chnrosnb_mod
15100clplatea
16100clplateb
17100clplatec
18100cosine
19100cragglvy
20100cragglvy2
21100curly
22100curly10
23100curly20
24100curly30
2599dixmaane
2699dixmaanf
2799dixmaang
2899dixmaanh
2999dixmaani
3099dixmaanj
3199dixmaank
3299dixmaanl
3399dixmaanm
3499dixmaann
3599dixmaano
3699dixmaanp
37100dixon3dq
38100dqdrtic
39100dqrtic
40100edensch
41100eg2
42100engval1
43100errinros_mod
44100extrosnb
45100fletcbv2
46100fletcbv3_mod
47100fletchcr
48100freuroth
49100genhumps
50100genrose
51100genrose_nash
52100indef_mod
53100integreq
54100liarwhd
55100morebv
56100ncb20
57100ncb20b
58100noncvxu2
59100noncvxun
60100nondia
61100nondquar
62100penalty1
63100penalty2
64100penalty3
65100powellsg
66100power
67100quartc
68100sbrybnd
69100schmvett
70100scosine
71100sinquad
72100sparsine
73100sparsqur
74100spmsrtls
75100srosenbr
76100tointgss
77100tquartic
78100tridia
79100vardim
80100woods

Then, one can prepare a list of problems using the selected ones.

using ADNLPModels
 adproblems = (
   eval(Meta.parse("ADNLPProblems.$(pb[:name])()")) for pb in eachrow(names_pb_vars)
@@ -64,4 +64,4 @@
   78 │   100  tridia
   79 │   100  vardim
   80 │   100  woods
-         65 rows omitted)
+ 65 rows omitted) diff --git a/dev/objects.inv b/dev/objects.inv index 23c9c111d14fbd2061a1cc2c848a68cbef7c6696..4a58dcd2dc09825f3207214322e3913979b3eeea 100644 GIT binary patch delta 1677 zcmV;826FlI4TcVojem37HW0n*S70Xf^u*XCWz+V}X=ajkn#SYio&iZ%f&+mv30hG) z{qF@xil$;K_2j*q1OcnXe!g7*+Lr38Xk2+&R)vUSA>LOjHFK@2YAwz5m)+R0_%wW) zt(23~DdGFExUQ?*sFf&;Y#M#53+Z%aXSuS)awY38(-q_3v4428`#7uBt*ZSuHJv#Q z9*djZ$5~NXSL?iW+AgM(r{l-sx5>BJ$F%(%Z- zzlZ}g#QOQ2@1{iBMt`&)uA^pao&0(kUAMIg1N;9n3i{Q7g-7Yt$6p{?j?^~A+) z#_XoGk&H%3M}HgbmeHXt>~Zp_9lvQ$M##q@5y%{p#B3_^$W<@f1DB#t~&T< zQ!!n={p-)`4^^qmDCKTw6w?bt{e(sk_&$}X)E}phvfrCL`=nNDBb{oZ+p3P-Qbks6 zqTALMVJFRxg)lA_ezEY2g*h`_EdcunTEto<0?VuZfnntc?^nBQP z$EY?8{@O&r-%B~fxbLFCFzYDECiBLnVmEY%(P!lR&Q!TH(HK;pgq?mu_w{+QJC}cb z_2K&6$A6Q=OJi-6xHFf2&9W-R6LJ5TjJt1T?R0_CSvJXO%ph{gAd=xkPUk3%zn5$I zg&>o8O4!W_N~5zI9l?(oH7@})WpF(4nY7fBfG`*kBPEC= zrIhb*9pr7TvT1qGX$it=9!$T2+k)EOw|_X;32Gvw!uW)MR2XH)DIp%fcH8yB35+nj zFu^;}zKBXbGnzCoCl4N)YeE&q=#<7M1bdBR2EO=N-|J51{Gy@?$EDW&jKATvXx|!z z3xlHaArY?h`!Xeixhd~U#zCX6u#L7bDhSXXK0xZ*W{lou3fBk9E*_+z9KjN5m46h@ zpo`YP?Z3RdXP8{CWY9KIMW>7|74Ac! zS|C_lGAD!;ivKX$C^6*{1EZ8XWnAol5u{o$nlZ3ARH4HN6fgdR!^_uTsK66JJ|5NA_7^;p?B z_@qAMFIXmB`+|`L@vDg)D}=0mL5K$_qkVD000!`8x+`aSL#Y%9lFeYr_B9wR+rn6r zD@kaWB@B!|XTSp-M)s+l(a)sxLSSQAD=UrLGMXU_z!T%=37tPn_&!MBg@2)DhGch) z{PXW2GA|}^l)X91-u~~OYU|XZR`9u(FLz*t_DJiTarq-*q&^sc=JhtWlv_fKkO&i5 zsjaFRxRKS|B3KH3OK7DSSrloY=@s}|n5OD;XGQt7l@de|i-UXw;z|nN~z73YE z&%fK!X0YK8FPEwUekIUb%zqr-SqJe6CKKZqB7L1eYX8BFknSV^NBG0@u+P5qhdY~n z1w<%OQ~a&j&?jPqt2rZFG4cSx1qjMvJfh9ueoC{JHKU){+bo<+h6@pdtAH_X`y8N~ zD`^PNM)4P)oFnppI|wugjlgpPD9lLBYsLu@31<`Fn>gn;YD)@v-d6O z9ulme4hrJWTKeW2?J*&qJoGK$Az{kskH+|I=}lFYa9`i!41xokvtB*G*tl3qZM|d| z009o#ee8#EhO4x7i-w@C4)hI>jek?)I1s((SEy=sYEK@p9Z2BD?o`d}*0Mvf+^1w)BmzYawoJ(U z`ju?QI0K=mdGD4eQm57GkJqi1wuO2UwJXkxG8a)S#Mg4ACa!U1rKK7FvKu=RH~pu{ zN;x^6622datE$Y5T8Z4qy4JTkmrj>VkCRH>s>**;av2tY6>xZc3zW^mlW471dkoUtcG?Gx<*! zmselD9e*cY8f&A(ojLbwmSrJci2H3a?xB&D(>Y3KStlnkgUAVk$P6cPGDT_pw_MA6 zf=s3JpjZWVPtR(Q^)Kyt4n^ib3XDEq$BYsfCCh5zGw}sUx&~)wvsAKx0gS9* z8h`$jP6=r26h5M0&IG`Kd&@42B#hMilwd>*SeW3))2qHFfMuAMb-7W-)UY{wZDByv zQ&{CP$4DRu0GHtj2;p=FgM%N^E;ic0y!2{+N3df?&PxDIGuWT_NLpw~Ko|^&krG6b zQqp%g5AwQJ+O&A!lmy{p?p42p%YxcIG=DhQ3CBbTh4C=~sW8%x6M{d0^|q~r6BuDw zVS-nneG-*?XB25*P98lp*MuyLQ7Mg&3Hlnx41Dpcw%6Xw`9*mdu1l@^6??;T(XKQK zCkA=xT_T+6cWKHD=B9Wk7#EE`!#3K&s31Ul_yVabn=xveDV!fDyLgm_as^AsRew@A zf-V{Zm;bWjfuVBUF%!ahjNjLLA~20M7&Ejsfz_=QZ36G8a|-ZXQd+nvy9HBs+s8+vQ-eY;$8x zrX-lmQQL7}{K3xSnot*kU|%P59008fnHC$#n~;eL?73xDI7VH(;x zwWt()1?4jjShgMLnKSw@5=In)v0zqhGfQdMVR$E)$VzQh#ei_EIuk)t@P?hf36*fn zjP#OH1jiq#cp|WC>I9oI+yUbq1fyy0jDhuWVB0=5gXQW&;kK|dSn>NuL}d;)>~|V7 zg?HAXZGy_gcyFSI?4(K@Tz`{ji}Eh>SMR7{$i9v%Qk{O;f2RHlBkXnM^18Sil{&T{ltAo+r_2I!o z-xA*RrHoQ#j5jWC%Cdk<>>g(j3}DWBfB<9Td?~f{l3@S@*lBmZAI2H3)YdI(!dcG= z{mZaa>U_C+RL*gvONfe$j>8xa@x8lbyQlXXy8ilKB4di;2k3QA4t%z EY^ypeM*si- diff --git a/dev/reference/index.html b/dev/reference/index.html index 53cf0e30..8384c9a9 100644 --- a/dev/reference/index.html +++ b/dev/reference/index.html @@ -1,2 +1,2 @@ -Reference · OptimizationProblems.jl
GitHub

Reference

​

Contents

​

​

Index

​

​

OptimizationProblems.meta — Constant

OptimizationProblems.meta A composite type that represents the main features of the optimization problem. optimize obj(x) subject to lvar ≤ x ≤ uvar lcon ≤ cons(x) ≤ ucon –- The following keys are valid:

  • nvar::Int: number of variables
  • variable_nvar::Bool: true if we can modify the number of variables
  • ncon::Int: number of general constraints
  • variable_ncon::Bool: true if we can modify the number of constraints
  • minimize::Bool: true if optimize == minimize
  • name::String: problem name
  • has_equalities_only::Bool: true if the problem has constraints, and all are equality constraints (doesn't include bounds)
  • has_inequalities_only::Bool: true if the problem has constraints, and all are inequality constraints (doesn't include bounds)
  • has_bounds::Bool: true if the problem has bound constraints
  • has_fixed_variables::Bool: true if it has fixed variables
  • objtype::Symbol: type of objective, in [:none, :constant, :linear, :quadratic, :sumofsquares, :other]
  • contype::Symbol: type of constraint, in [:unconstrained, :linear, :quadratic, :general]
  • best_known_lower_bound::Real: lower bound on the global optimal value (default: -Inf for minimization problem, f(x0) for maximization problem if x0 is feasible, -Inf otherwise)
  • best_known_upper_bound::Real: upper bound on the global optimal value (default: Inf for maximization problem, f(x0) for minimization problem if x0 is feasible, Inf otherwise)
  • is_feasible::Union{Bool, Missing}: true if problem is feasible
  • origin::Symbol: origin of the problem, in [:academic, :modelling, :real, :unknown]
source
+Reference · OptimizationProblems.jl
GitHub

Reference

​

Contents

​

​

Index

​

​

OptimizationProblems.meta — Constant

OptimizationProblems.meta A composite type that represents the main features of the optimization problem. optimize obj(x) subject to lvar ≤ x ≤ uvar lcon ≤ cons(x) ≤ ucon –- The following keys are valid:

  • nvar::Int: number of variables
  • variable_nvar::Bool: true if we can modify the number of variables
  • ncon::Int: number of general constraints
  • variable_ncon::Bool: true if we can modify the number of constraints
  • minimize::Bool: true if optimize == minimize
  • name::String: problem name
  • has_equalities_only::Bool: true if the problem has constraints, and all are equality constraints (doesn't include bounds)
  • has_inequalities_only::Bool: true if the problem has constraints, and all are inequality constraints (doesn't include bounds)
  • has_bounds::Bool: true if the problem has bound constraints
  • has_fixed_variables::Bool: true if it has fixed variables
  • objtype::Symbol: type of objective, in [:none, :constant, :linear, :quadratic, :sumofsquares, :other]
  • contype::Symbol: type of constraint, in [:unconstrained, :linear, :quadratic, :general]
  • best_known_lower_bound::Real: lower bound on the global optimal value (default: -Inf for minimization problem, f(x0) for maximization problem if x0 is feasible, -Inf otherwise)
  • best_known_upper_bound::Real: upper bound on the global optimal value (default: Inf for maximization problem, f(x0) for minimization problem if x0 is feasible, Inf otherwise)
  • is_feasible::Union{Bool, Missing}: true if problem is feasible
  • origin::Symbol: origin of the problem, in [:academic, :modelling, :real, :unknown]
source
diff --git a/dev/search_index.js b/dev/search_index.js index 796d4953..0c08a124 100644 --- a/dev/search_index.js +++ b/dev/search_index.js @@ -1,3 +1,3 @@ var documenterSearchIndex = {"docs": -[{"location":"contributing/#Contributing-to-OptimizationProblems.jl","page":"Contributing","title":"Contributing to OptimizationProblems.jl","text":"","category":"section"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"First off, thanks for taking the time to contribute!","category":"page"},{"location":"contributing/#Bug-reports-and-discussions","page":"Contributing","title":"Bug reports and discussions","text":"","category":"section"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"If you think you found a bug, feel free to open an issue. Focused suggestions and requests can also be opened as issues. Before opening a pull request, please start an issue or a discussion.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"If you want to ask a question not suited for a bug report, feel free to start a discussion here, a forum for general discussion about this repository and the JuliaSmoothOptimizers organization. Discussions about any of our packages are welcome.","category":"page"},{"location":"contributing/#Adding-new-problems","page":"Contributing","title":"Adding new problems","text":"","category":"section"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"We welcome pull requests proposing new problems to the problem set. As a general guideline, a pull request should concern one problem only. We recommend checking existing problems as a template for your new problems.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"Here is a to-do list, to help you add new problems:","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"Before implementing a new problem, make sure it does not already exist in this repository.\nThis package contains implementations using JuMP and ADNLPModels. A pull request should include both implementations of a new problem. Additionally, a \"meta\" provides general information regarding the problem. Therefore, a PR adding a new problem should contain 3 files:\nsrc/ADNLPProblems/problem_name.jl\nsrc/PureJuMP/problem_name.jl\nsrc/Meta/problem_name.jl","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"In both cases, the function must have the same name problem_name as the file.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"When submitting a problem, please pay particular attention to the documentation. We would like to gather as much information as possible on the provenance of problems, other problem sets where the problems are present, and general information on the problem. ","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"The documentation should be added to the file in the PureJuMP folder.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"New problems can be scalable, see ADNLPProblems/arglina.jl and PureJuMP/arglina.jl for examples. In that case, the first keyword parameter should be the number of variables n::Int and have the default value default_nvar (constant predefined in the module). If your problem has restrictions on the number of variables, e.g., n should be odd, or n should have the form 4k + 3, then, instead of throwing errors when the restrictions are not satisfied, you should instead use the number of variables to be as close to n as possible. For example, if you want n odd and n = 100 is passed, you can internally convert to n = 99. If you want n = 4k + 3, and n = 100 is passed, then compute k = round(Int, (n - 3) / 4) and update n.\nA first version of the meta can be generated using generate_meta. A String is returned that can be copy-pasted into the Meta folder, and then edited.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":" using ADNLPModels, NLPModels, NLPModelsJuMP, OptimizationProblems\n include(\"test/utils.jl\")\n # there must exists a function `problem_name` which loads the model in the environment\n name = \"problem_name\"\n open(\"$name.jl\", \"w\") do io\n print(io, generate_meta(name))\n end","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"Problems modeled with ADNLPModels should be type-stable, i.e. they should all have keyword argument type::Type{T} = Float64 where T is the type of the initial guess and the type used by the NLPModel API.","category":"page"},{"location":"contributing/#Templates-for-the-new-functions","page":"Contributing","title":"Templates for the new functions","text":"","category":"section"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"In order to standardize the new functions, we offer here a template for both AD and JuMP models.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"First, we describe the PureJuMP file function_name.jl. This file contains the documentation on the problem.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"# Full name of the problem (while function_name could be an abbreviation)\n#\n# Source of the problem\n# Don't hesitate to put more than one source if it is mentioned elsewhere\n#\n# CUTEst classification (if available)\n#\n# other information related to the problem\n#\n\nexport function_name\n\n\"A short docstring on the problem\"\nfunction function_name(; n::Int = default_nvar, kwargs...)\n nlp = Model()\n # define the model: TODO\n return nlp\nend","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"Next, we describe the ADNLPProblems file function_name.jl.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"export function_name\n\nfunction function_name(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T} \n # define f \n # define x0\n # nlp = ADNLPModels.ADNLPModel(f, x0, name = \"function_name\"; kwargs...)\n return nlp\nend","category":"page"},{"location":"meta/#OptimizationProblems.jl-problem-classification","page":"Problem classification","title":"OptimizationProblems.jl problem classification","text":"","category":"section"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"It is possible to access information on the problems implemented in OptimizationProblems.jl without loading the problems using the package's own classification. ​","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"using OptimizationProblems","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"Each problem has its own metadata structure, and there is a global metadata structure regrouping all the information.","category":"page"},{"location":"meta/#Problem's-metadata","page":"Problem classification","title":"Problem's metadata","text":"","category":"section"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"Each problem's metadata is accessible with OptimizationProblems.nameoftheproblem_meta and regroups in a Dict most of the essential information regarding each problem.","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"OptimizationProblems.AMPGO02_meta","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"See ? OptimizationProblems.meta for more documentation on the various entries and their default values.","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"This structre is completed by getters to access the number of variables, get_nameoftheproblem_nvar,the number of constraints, get_nameoftheproblem_ncon, the number of linear constraints, get_nameoftheproblem_nlin, the number of nonlinear constraints, get_nameoftheproblem_nnln, the number of equality constraints, get_nameoftheproblem_nequ, and the number of inequality constraints, get_nameoftheproblem_nineq.","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"OptimizationProblems.get_AMPGO02_nvar()","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"For scalable problems the entry :variable_nvar (and/or :variable_ncon) is set as true and one can access the number of variables by passing the parameters to the getter functions. By default, the number of variables set in the meta is obtained using OptimizationProblems.default_nvar as a parameter to define the problem.","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"OptimizationProblems.arglina_meta","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"OptimizationProblems.get_arglina_nvar(n = 10)","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"OptimizationProblems.PureJuMP.arglina(n = 10)","category":"page"},{"location":"meta/#Global-meta","page":"Problem classification","title":"Global meta","text":"","category":"section"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"This package collects all the metadata in a single DataFrame.","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"OptimizationProblems.meta","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"Then, it is very simple to filter problems using queries on DataFrame. We refer to the documentation of DataFrames.jl for tutorials. For instance, if one wants to select unconstrained scalable problems and use (:nvar, :name).","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"meta = OptimizationProblems.meta\nnames_pb_vars = meta[(meta.variable_nvar .== true) .& (meta.ncon .== 0), [:nvar, :name]]","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"Then, one can prepare a list of problems using the selected ones.","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"using ADNLPModels\nadproblems = (\n eval(Meta.parse(\"ADNLPProblems.$(pb[:name])()\")) for pb in eachrow(names_pb_vars)\n)","category":"page"},{"location":"reference/#Reference","page":"Reference","title":"Reference","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"​","category":"page"},{"location":"reference/#Contents","page":"Reference","title":"Contents","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"​","category":"page"},{"location":"reference/","page":"Reference","title":"Reference","text":"Pages = [\"reference.md\"]","category":"page"},{"location":"reference/","page":"Reference","title":"Reference","text":"​","category":"page"},{"location":"reference/#Index","page":"Reference","title":"Index","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"​","category":"page"},{"location":"reference/","page":"Reference","title":"Reference","text":"Pages = [\"reference.md\"]","category":"page"},{"location":"reference/","page":"Reference","title":"Reference","text":"​","category":"page"},{"location":"reference/","page":"Reference","title":"Reference","text":"Modules = [OptimizationProblems, OptimizationProblems.PureJuMP]","category":"page"},{"location":"reference/#OptimizationProblems.meta","page":"Reference","title":"OptimizationProblems.meta","text":"OptimizationProblems.meta A composite type that represents the main features of the optimization problem. optimize obj(x) subject to lvar ≤ x ≤ uvar lcon ≤ cons(x) ≤ ucon –- The following keys are valid:\n\nnvar::Int: number of variables\nvariable_nvar::Bool: true if we can modify the number of variables\nncon::Int: number of general constraints\nvariable_ncon::Bool: true if we can modify the number of constraints\nminimize::Bool: true if optimize == minimize\nname::String: problem name\nhas_equalities_only::Bool: true if the problem has constraints, and all are equality constraints (doesn't include bounds)\nhas_inequalities_only::Bool: true if the problem has constraints, and all are inequality constraints (doesn't include bounds)\nhas_bounds::Bool: true if the problem has bound constraints\nhas_fixed_variables::Bool: true if it has fixed variables\nobjtype::Symbol: type of objective, in [:none, :constant, :linear, :quadratic, :sumofsquares, :other]\ncontype::Symbol: type of constraint, in [:unconstrained, :linear, :quadratic, :general]\nbest_known_lower_bound::Real: lower bound on the global optimal value (default: -Inf for minimization problem, f(x0) for maximization problem if x0 is feasible, -Inf otherwise)\nbest_known_upper_bound::Real: upper bound on the global optimal value (default: Inf for maximization problem, f(x0) for minimization problem if x0 is feasible, Inf otherwise)\nis_feasible::Union{Bool, Missing}: true if problem is feasible\norigin::Symbol: origin of the problem, in [:academic, :modelling, :real, :unknown]\n\n\n\n\n\n","category":"constant"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO02-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO02","text":"Univariate multimodal minimization problem AMPGO02\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO03-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO03","text":"Univariate multimodal minimization problem AMPGO03\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO04-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO04","text":"Univariate multimodal minimization problem AMPGO04\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO05-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO05","text":"Univariate multimodal minimization problem AMPGO05\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO06-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO06","text":"Univariate multimodal minimization problem AMPGO06\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO07-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO07","text":"Univariate multimodal minimization problem AMPGO07\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO08-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO08","text":"Univariate multimodal minimization problem AMPGO08\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO09-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO09","text":"Univariate multimodal minimization problem AMPGO09\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO10-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO10","text":"Univariate multimodal minimization problem AMPGO10\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO11-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO11","text":"Univariate multimodal minimization problem AMPGO11\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO12-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO12","text":"Univariate multimodal minimization problem AMPGO12\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO13-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO13","text":"Univariate multimodal minimization problem AMPGO13\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO14-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO14","text":"Univariate multimodal minimization problem AMPGO14\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO15-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO15","text":"Univariate multimodal minimization problem AMPGO15\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO18-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO18","text":"Univariate multimodal minimization problem AMPGO18\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO20-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO20","text":"Univariate multimodal minimization problem AMPGO20\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO21-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO21","text":"Univariate multimodal minimization problem AMPGO21\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO22-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO22","text":"Univariate multimodal minimization problem AMPGO22\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Dus2_1-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Dus2_1","text":"Univariate unimodal minimization problem Dus2_1\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Dus2_3-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Dus2_3","text":"Univariate unimodal minimization problem Dus2_3\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Dus2_9-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Dus2_9","text":"Univariate multimodal minimization problem Dus2_9\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Duscube-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Duscube","text":"Univariate multimodal minimization problem Duscube\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Shpak1-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Shpak1","text":"Univariate multimodal minimization problem Shpak1\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Shpak2-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Shpak2","text":"Univariate multimodal minimization problem Shpak2\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Shpak3-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Shpak3","text":"Univariate multimodal minimization problem Shpak3\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Shpak4-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Shpak4","text":"Univariate multimodal minimization problem Shpak4\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Shpak5-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Shpak5","text":"Univariate multimodal minimization problem Shpak5\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Shpak6-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Shpak6","text":"Univariate multimodal minimization problem Shpak6\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.arglina-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.arglina","text":"Linear function with n parameters and m observations - full rank\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.arglinb-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.arglinb","text":"Linear function with n parameters and m observations - rank 1\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.arglinc-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.arglinc","text":"Linear function with n parameters and m observations - rank 1, zero columns and rows\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.argtrig-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.argtrig","text":"Linear function with n parameters and m observations - full rank\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.arwhead-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.arwhead","text":"Arrow head model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.bard-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.bard","text":"Linear function with n parameters and m observations - full rank\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.bdqrtic-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.bdqrtic","text":"Banded quartic model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.beale-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.beale","text":"Beale Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.brownal-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.brownal","text":"Brownbs Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.brownbs-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.brownbs","text":"Brownbs Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.brownden-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.brownden","text":"Brown and Dennis function\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.broydn7d-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.broydn7d","text":"Broyden 7-diagonal model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.brybnd-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.brybnd","text":"Broyden banded model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.bt1-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.bt1","text":"BT1 Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.chainwoo-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.chainwoo","text":"The chained Woods function in size n, a variant on the Woods function\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.cliff-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.cliff","text":"The 'cliff problem' in 2 variables\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.clnlbeam-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.clnlbeam","text":"The clnlbeam problem in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.clplatea-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.clplatea","text":"The clamped plate problem (Strang, Nocedal, Dax).\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.clplateb-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.clplateb","text":"The clamped plate problem (Strang, Nocedal, Dax).\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.clplatec-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.clplatec","text":"The clamped plate problem (Strang, Nocedal, Dax).\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.cosine-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.cosine","text":"The cosine function in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.cragglvy-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.cragglvy","text":"The extented Cragg and Levy function in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.cragglvy2-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.cragglvy2","text":"The extented Cragg and Levy function in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.curly-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.curly","text":"Curly function in size n with semi-bandwidth b\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.curly10-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.curly10","text":"Curly function in size n with semi-bandwidth 10\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.curly20-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.curly20","text":"Curly function in size n with semi-bandwidth 20\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.curly30-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.curly30","text":"Curly function in size n with semi-bandwidth 30\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaane-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaane","text":"Dixon-Maany function in size n (version E by default)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaanf-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaanf","text":"Dixon-Maany function in size n (version F)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaang-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaang","text":"Dixon-Maany function in size n (version G)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaanh-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaanh","text":"Dixon-Maany function in size n (version H)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaani-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaani","text":"Dixon-Maany function in size n (version I by default)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaanj-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaanj","text":"Dixon-Maany function in size n (version J)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaank-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaank","text":"Dixon-Maany function in size n (version K)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaanl-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaanl","text":"Dixon-Maany function in size n (version L)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaanm-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaanm","text":"Dixon-Maany function in size n (version M by default)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaann-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaann","text":"Dixon-Maany function in size n (version N)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaano-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaano","text":"Dixon-Maany function in size n (version O)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaanp-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaanp","text":"Dixon-Maany function in size n (version P)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixon3dq-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixon3dq","text":"Dixon's tridiagonal quadratic.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dqdrtic-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dqdrtic","text":"Diagonal quadratic problem\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dqrtic-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dqrtic","text":"Diagonal quartic model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.edensch-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.edensch","text":"Extended Dennis-Schnabel model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.eg2-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.eg2","text":"model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.engval1-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.engval1","text":"The Engval1 model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.genrose-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.genrose","text":"Generalized Rosenbrock model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.genrose_nash-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.genrose_nash","text":"Nash's variant of genrose() in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.gulf-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.gulf","text":"Linear function with n parameters and m observations - full rank\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs1-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs1","text":"HS1 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs10-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs10","text":"HS10 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs100-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs100","text":"HS100 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs101-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs101","text":"HS101 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs102-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs102","text":"HS102 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs103-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs103","text":"HS103 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs104-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs104","text":"HS104 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs105-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs105","text":"HS105 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs106-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs106","text":"HS106 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs107-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs107","text":"HS107 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs108-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs108","text":"HS108 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs109-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs109","text":"HS109 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs11-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs11","text":"HS11 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs110-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs110","text":"HS110 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs111-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs111","text":"HS111 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs112-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs112","text":"HS112 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs113-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs113","text":"HS113 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs114-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs114","text":"HS114 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs116-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs116","text":"HS116 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs117-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs117","text":"HS117 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs118-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs118","text":"HS118 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs119-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs119","text":"HS119 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs12-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs12","text":"HS12 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs13-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs13","text":"HS13 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs14-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs14","text":"HS14 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs15-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs15","text":"HS15 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs16-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs16","text":"HS16 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs17-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs17","text":"HS17 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs18-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs18","text":"HS18 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs19-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs19","text":"HS19 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs2-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs2","text":"HS2 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs20-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs20","text":"HS20 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs201-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs201","text":"HS201 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs21-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs21","text":"HS21 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs22-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs22","text":"HS22 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs220-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs220","text":"HS220 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs221-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs221","text":"HS221 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs222-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs222","text":"HS222 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs223-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs223","text":"HS223 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs224-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs224","text":"HS224 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs225-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs225","text":"HS225 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs226-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs226","text":"HS226 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs227-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs227","text":"HS227 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs228-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs228","text":"HS228 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs229-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs229","text":"HS229 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs23-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs23","text":"HS23 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs230-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs230","text":"HS230 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs231-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs231","text":"HS231 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs232-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs232","text":"HS232 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs233-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs233","text":"HS233 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs234-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs234","text":"HS234 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs235-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs235","text":"HS235 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs236-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs236","text":"HS236 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs237-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs237","text":"HS237 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs238-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs238","text":"HS238 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs239-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs239","text":"HS239 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs24-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs24","text":"HS24 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs240-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs240","text":"HS240 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs241-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs241","text":"HS241 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs242-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs242","text":"HS242 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs243-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs243","text":"HS243 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs244-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs244","text":"HS244 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs245-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs245","text":"HS245 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs246-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs246","text":"HS246 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs248-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs248","text":"HS248 model: around the world problem\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs249-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs249","text":"HS249 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs25-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs25","text":"HS25 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs250-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs250","text":"HS250 model: Rosenbrock's post office problem\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs251-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs251","text":"HS251 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs252-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs252","text":"HS252 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs253-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs253","text":"HS253 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs254-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs254","text":"HS254 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs255-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs255","text":"HS255 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs256-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs256","text":"HS256 model: Powell's function\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs257-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs257","text":"HS257 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs258-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs258","text":"HS258 model: Powell's function\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs259-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs259","text":"HS259 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs26-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs26","text":"HS26 Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs260-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs260","text":"HS260 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs261-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs261","text":"HS261 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs262-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs262","text":"HS262 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs263-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs263","text":"HS263 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs264-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs264","text":"HS264 model: modified Rosen-Suzuki problem\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs265-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs265","text":"HS265 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs27-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs27","text":"HS27 Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs28-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs28","text":"HS28 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs29-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs29","text":"HS29 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs3-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs3","text":"HS3 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs30-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs30","text":"HS30 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs31-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs31","text":"HS31 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs316-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs316","text":"HS316 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs317-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs317","text":"HS317 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs318-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs318","text":"HS318 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs319-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs319","text":"HS319 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs32-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs32","text":"HS32 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs320-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs320","text":"HS320 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs321-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs321","text":"HS321 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs322-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs322","text":"HS322 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs33-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs33","text":"HS33 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs34-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs34","text":"HS34 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs35-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs35","text":"HS35 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs36-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs36","text":"HS36 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs37-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs37","text":"HS37 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs378-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs378","text":"HS378 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs38-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs38","text":"HS38 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs39-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs39","text":"HS39 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs4-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs4","text":"HS4 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs40-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs40","text":"HS40 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs41-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs41","text":"HS41 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs42-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs42","text":"HS42 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs43-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs43","text":"HS43 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs44-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs44","text":"HS44 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs45-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs45","text":"HS45 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs46-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs46","text":"HS46 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs47-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs47","text":"HS47 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs48-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs48","text":"HS48 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs49-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs49","text":"HS49 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs5-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs5","text":"HS5 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs50-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs50","text":"HS50 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs51-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs51","text":"HS51 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs52-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs52","text":"HS52 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs53-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs53","text":"HS53 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs54-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs54","text":"HS54 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs55-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs55","text":"HS55 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs56-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs56","text":"HS56 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs57-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs57","text":"HS57 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs59-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs59","text":"HS59 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs6-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs6","text":"HS6 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs60-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs60","text":"HS60 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs61-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs61","text":"HS61 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs62-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs62","text":"HS62 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs63-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs63","text":"HS63 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs64-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs64","text":"HS64 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs65-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs65","text":"HS65 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs66-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs66","text":"HS66 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs7-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs7","text":"HS7 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs70-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs70","text":"HS70 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs71-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs71","text":"HS71 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs72-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs72","text":"HS72 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs73-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs73","text":"HS73 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs74-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs74","text":"HS74 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs75-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs75","text":"HS75 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs76-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs76","text":"HS76 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs77-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs77","text":"HS77 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs78-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs78","text":"HS78 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs79-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs79","text":"HS79 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs8-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs8","text":"HS8 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs80-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs80","text":"HS80 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs81-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs81","text":"HS81 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs83-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs83","text":"HS83 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs84-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs84","text":"HS84 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs86-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs86","text":"HS86 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs87-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs87","text":"HS87 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs9-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs9","text":"HS9 Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs93-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs93","text":"HS93 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs95-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs95","text":"HS95 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs96-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs96","text":"HS96 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs97-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs97","text":"HS97 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs98-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs98","text":"HS98 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs99-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs99","text":"HS99 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.integreq-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.integreq","text":"Linear function with n parameters and m observations - full rank\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.meyer3-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.meyer3","text":"Meyer function\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.nasty-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.nasty","text":"Nasty problem\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer1c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer1c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer1d-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer1d","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer2c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer2c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer3c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer3c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer4c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer4c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer5c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer5c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer5d-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer5d","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer6c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer6c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer7c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer7c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer8c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer8c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.penalty1-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.penalty1","text":"Linear function with n parameters and m observations - full rank\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.penalty2-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.penalty2","text":"A penalty problem by Gill, Murray and Pitfield in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.penalty3-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.penalty3","text":"A penalty problem by Gill, Murray and Pitfield in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.powellsg-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.powellsg","text":"The extended Powell singular problem in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.power-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.power","text":"The Power problem by Oren.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.quartc-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.quartc","text":"A simple quartic function.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.rosenbrock-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.rosenbrock","text":"Classic Rosenbrock problem\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.sbrybnd-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.sbrybnd","text":"Broyden banded system of nonlinear equations in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.schmvett-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.schmvett","text":"Another function with nontrivial groups and repetitious elements in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.scosine-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.scosine","text":"Another function with nontrivial groups and repetitious elements in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.sinquad-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.sinquad","text":"Another function with nontrivial groups and repetitious elements in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.sparsine-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.sparsine","text":"A sparse problem involving sine functions in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.sparsqur-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.sparsqur","text":"A sparse quartic problem in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.srosenbr-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.srosenbr","text":"The separable extension of Rosenbrock's function 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.tointgss-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.tointgss","text":"Toint's Gaussian problem in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.tquartic-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.tquartic","text":"A quartic function with nontrivial groups and repetitious elements in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.tridia-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.tridia","text":"Shanno's TRIDIA quadratic tridiagonal problem.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.vardim-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.vardim","text":"Variable dimension problem.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.woods-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.woods","text":"The extended Woods problem n \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.zangwil3-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.zangwil3","text":"Zangwil3 Model\n\n\n\n\n\n","category":"method"},{"location":"benchmark/#Run-a-benchmark-with-OptimizationProblems.jl","page":"Benchmark","title":"Run a benchmark with OptimizationProblems.jl","text":"","category":"section"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"In this more advanced tutorial, we use the problems from OptimizationProblems to run a benchmark for unconstrained problems. The tutorial will use:","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"JSOSolvers: This package provides optimization solvers in pure Julia for unconstrained and bound-constrained optimization.\nNLPModelsJuMP: This package convert JuMP model in NLPModel format.\nSolverBenchmark: This package provides general tools for benchmarking solvers.","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"using JSOSolvers, NLPModels, NLPModelsJuMP, OptimizationProblems, SolverBenchmark\nusing OptimizationProblems.PureJuMP","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"We select the problems from PureJuMP submodule of OptimizationProblems converted in NLPModels using NLPModelsJuMP.","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"problems = (MathOptNLPModel(OptimizationProblems.PureJuMP.eval(Meta.parse(problem))(), name=problem) for problem ∈ OptimizationProblems.meta[!, :name])","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"The same can be achieved using OptimizationProblems.ADNLPProblems instead of OptimizationProblems.PureJuMP as follows:","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"using ADNLPModels\nusing OptimizationProblems.ADNLPProblems\nad_problems = (OptimizationProblems.ADNLPProblems.eval(Meta.parse(problem))() for problem ∈ OptimizationProblems.meta[!, :name])","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"We also define a dictionary of solvers that will be used for our benchmark. We consider here JSOSolvers.lbfgs and JSOSolvers.trunk.","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"solvers = Dict(\n :lbfgs => model -> lbfgs(model, mem=5, atol=1e-5, rtol=0.0),\n :trunk => model -> trunk(model, atol=1e-5, rtol=0.0),\n)","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"The function SolverBenchmark.bmak_solvers will run all the problems on the specified solvers and store the results in a DataFrame. At this stage, we discard the problems that have constraints or bounds using !unconstrained(prob), and those that are too large or too small with get_nvar(prob) > 100 || get_nvar(prob) < 5.","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"stats = bmark_solvers(\n solvers, problems,\n skipif=prob -> (!unconstrained(prob) || get_nvar(prob) > 100 || get_nvar(prob) < 5),\n)","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"We can explore the results solver by solver in stats[:lbfgs] and stats[:trunk], or get a profile wall using SolverBenchmark.profile_solvers.","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"cols = [:id, :name, :nvar, :objective, :dual_feas, :neval_obj, :neval_grad, :neval_hess, :iter, :elapsed_time, :status]\nheader = Dict(\n :nvar => \"n\",\n :objective => \"f(x)\",\n :dual_feas => \"‖∇f(x)‖\",\n :neval_obj => \"# f\",\n :neval_grad => \"# ∇f\",\n :neval_hess => \"# ∇²f\",\n :elapsed_time => \"t\",\n)\n\nfor solver ∈ keys(solvers)\n pretty_stats(stats[solver][!, cols], hdr_override=header)\nend","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"first_order(df) = df.status .== :first_order\nunbounded(df) = df.status .== :unbounded\nsolved(df) = first_order(df) .| unbounded(df)\ncostnames = [\"time\", \"obj + grad + hess\"]\ncosts = [\n df -> .!solved(df) .* Inf .+ df.elapsed_time,\n df -> .!solved(df) .* Inf .+ df.neval_obj .+ df.neval_grad .+ df.neval_hess,\n]\n\nusing Plots\ngr()\n\nprofile_solvers(stats, costs, costnames)","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"It is also possible to select problems when initializing the problem list by filtering OptimizationProblems.meta:","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"meta = OptimizationProblems.meta\nproblem_list = meta[(meta.ncon .== 0) .& .!meta.has_bounds .& (5 .<= meta.nvar .<= 100), :name]\nproblems = (MathOptNLPModel(eval(Meta.parse(problem))(), name=problem) for problem ∈ problem_list)","category":"page"},{"location":"#OptimizationProblems.jl","page":"Home","title":"OptimizationProblems.jl","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"This package provides a collection of optimization problems in JuMP and ADNLPModels syntax.","category":"page"},{"location":"#Installing","page":"Home","title":"Installing","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"OptimizationProblems can be installed and tested through the Julia package manager:","category":"page"},{"location":"","page":"Home","title":"Home","text":"julia> ]\npkg> add OptimizationProblems\npkg> test OptimizationProblems","category":"page"},{"location":"#How-to-cite","page":"Home","title":"How to cite","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"If you use OptimizationProblems.jl in your work, please cite using the format given in CITATION.cff.","category":"page"},{"location":"#Bug-reports-and-discussions","page":"Home","title":"Bug reports and discussions","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"If you think you found a bug, feel free to open an issue. Focused suggestions and requests can also be opened as issues. Before opening a pull request, start an issue or a discussion on the topic, please.","category":"page"},{"location":"","page":"Home","title":"Home","text":"If you want to ask a question not suited for a bug report, feel free to start a discussion here. This forum is for general discussion about this repository and the JuliaSmoothOptimizers, so questions about any of our packages are welcome.","category":"page"},{"location":"tutorial/#OptimizationProblems.jl-Tutorial","page":"Tutorial","title":"OptimizationProblems.jl Tutorial","text":"","category":"section"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"In this tutorial, we will see how to access the problems in JuMP and ADNLPModel syntax. This package is subdivided in two submodules: PureJuMP for the JuMP problems, ADNLPProblems for the ADNLPModel problems.","category":"page"},{"location":"tutorial/#Problems-in-JuMP-syntax:-PureJuMP","page":"Tutorial","title":"Problems in JuMP syntax: PureJuMP","text":"","category":"section"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"You can obtain the list of problems currently defined with OptimizationProblems.meta[!, :name].","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"using OptimizationProblems, OptimizationProblems.PureJuMP\nproblems = OptimizationProblems.meta[!, :name]\nlength(problems)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"Then, it suffices to select any of this problem to get the JuMP model.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"jump_model = zangwil3()","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"Note that certain problems are scalable, i.e., their size depends on parameters that can be modified. The list of those problems is available once again using meta:","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"var_problems = OptimizationProblems.meta[OptimizationProblems.meta.variable_nvar, :name]\nlength(var_problems)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"Then, using the keyword n, it is possible to specify the targeted number of variables.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"jump_model_12 = woods(n=12)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"jump_model_120 = woods(n=120)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"These problems can be converted as NLPModels via NLPModelsJuMP to facilitate evaluating objective, constraints and their derivatives.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"using NLPModels, NLPModelsJuMP\nnlp_model_120 = MathOptNLPModel(jump_model_120)\nobj(nlp_model_120, zeros(120))","category":"page"},{"location":"tutorial/#Problems-in-ADNLPModel-syntax:-ADNLPProblems","page":"Tutorial","title":"Problems in ADNLPModel syntax: ADNLPProblems","text":"","category":"section"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"This package also offers ADNLPModel test problems. This is an optional dependency, so ADNLPModels has to be added first.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"using ADNLPModels","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"You can obtain the list of problems currently defined with OptimizationProblems.meta[!, :name]:","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"using OptimizationProblems, OptimizationProblems.ADNLPProblems\nproblems = OptimizationProblems.meta[!, :name]\nlength(problems)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"Similarly, to the PureJuMP models, it suffices to select any of this problem to get the model.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"nlp = zangwil3()","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"Note that some of these problems are scalable, i.e., their size depends on some parameters that can be modified.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"nlp_12 = woods(n=12)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"nlp_120 = woods(n=120)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"One of the advantages of these problems is that they are type-stable. Indeed, one can specify the output type with the keyword type as follows.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"nlp16_12 = woods(n=12, type=Float16)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"Then, all the API will be compatible with the precised type.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"using NLPModels\nobj(nlp16_12, zeros(Float16, 12))","category":"page"}] +[{"location":"contributing/#Contributing-to-OptimizationProblems.jl","page":"Contributing","title":"Contributing to OptimizationProblems.jl","text":"","category":"section"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"First off, thanks for taking the time to contribute!","category":"page"},{"location":"contributing/#Bug-reports-and-discussions","page":"Contributing","title":"Bug reports and discussions","text":"","category":"section"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"If you think you found a bug, feel free to open an issue. Focused suggestions and requests can also be opened as issues. Before opening a pull request, please start an issue or a discussion.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"If you want to ask a question not suited for a bug report, feel free to start a discussion here, a forum for general discussion about this repository and the JuliaSmoothOptimizers organization. Discussions about any of our packages are welcome.","category":"page"},{"location":"contributing/#Adding-new-problems","page":"Contributing","title":"Adding new problems","text":"","category":"section"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"We welcome pull requests proposing new problems to the problem set. As a general guideline, a pull request should concern one problem only. We recommend checking existing problems as a template for your new problems.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"Here is a to-do list, to help you add new problems:","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"Before implementing a new problem, make sure it does not already exist in this repository.\nThis package contains implementations using JuMP and ADNLPModels. A pull request should include both implementations of a new problem. Additionally, a \"meta\" provides general information regarding the problem. Therefore, a PR adding a new problem should contain 3 files:\nsrc/ADNLPProblems/problem_name.jl\nsrc/PureJuMP/problem_name.jl\nsrc/Meta/problem_name.jl","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"In both cases, the function must have the same name problem_name as the file.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"When submitting a problem, please pay particular attention to the documentation. We would like to gather as much information as possible on the provenance of problems, other problem sets where the problems are present, and general information on the problem. ","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"The documentation should be added to the file in the PureJuMP folder.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"New problems can be scalable, see ADNLPProblems/arglina.jl and PureJuMP/arglina.jl for examples. In that case, the first keyword parameter should be the number of variables n::Int and have the default value default_nvar (constant predefined in the module). If your problem has restrictions on the number of variables, e.g., n should be odd, or n should have the form 4k + 3, then, instead of throwing errors when the restrictions are not satisfied, you should instead use the number of variables to be as close to n as possible. For example, if you want n odd and n = 100 is passed, you can internally convert to n = 99. If you want n = 4k + 3, and n = 100 is passed, then compute k = round(Int, (n - 3) / 4) and update n.\nA first version of the meta can be generated using generate_meta. A String is returned that can be copy-pasted into the Meta folder, and then edited.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":" using ADNLPModels, NLPModels, NLPModelsJuMP, OptimizationProblems\n include(\"test/utils.jl\")\n # there must exists a function `problem_name` which loads the model in the environment\n name = \"problem_name\"\n open(\"$name.jl\", \"w\") do io\n print(io, generate_meta(name))\n end","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"Problems modeled with ADNLPModels should be type-stable, i.e. they should all have keyword argument type::Type{T} = Float64 where T is the type of the initial guess and the type used by the NLPModel API.","category":"page"},{"location":"contributing/#Templates-for-the-new-functions","page":"Contributing","title":"Templates for the new functions","text":"","category":"section"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"In order to standardize the new functions, we offer here a template for both AD and JuMP models.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"First, we describe the PureJuMP file function_name.jl. This file contains the documentation on the problem.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"# Full name of the problem (while function_name could be an abbreviation)\n#\n# Source of the problem\n# Don't hesitate to put more than one source if it is mentioned elsewhere\n#\n# CUTEst classification (if available)\n#\n# other information related to the problem\n#\n\nexport function_name\n\n\"A short docstring on the problem\"\nfunction function_name(; n::Int = default_nvar, kwargs...)\n nlp = Model()\n # define the model: TODO\n return nlp\nend","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"Next, we describe the ADNLPProblems file function_name.jl.","category":"page"},{"location":"contributing/","page":"Contributing","title":"Contributing","text":"export function_name\n\nfunction function_name(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T} \n # define f \n # define x0\n # nlp = ADNLPModels.ADNLPModel(f, x0, name = \"function_name\"; kwargs...)\n return nlp\nend","category":"page"},{"location":"meta/#OptimizationProblems.jl-problem-classification","page":"Problem classification","title":"OptimizationProblems.jl problem classification","text":"","category":"section"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"It is possible to access information on the problems implemented in OptimizationProblems.jl without loading the problems using the package's own classification. ​","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"using OptimizationProblems","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"Each problem has its own metadata structure, and there is a global metadata structure regrouping all the information.","category":"page"},{"location":"meta/#Problem's-metadata","page":"Problem classification","title":"Problem's metadata","text":"","category":"section"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"Each problem's metadata is accessible with OptimizationProblems.nameoftheproblem_meta and regroups in a Dict most of the essential information regarding each problem.","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"OptimizationProblems.AMPGO02_meta","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"See ? OptimizationProblems.meta for more documentation on the various entries and their default values.","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"This structre is completed by getters to access the number of variables, get_nameoftheproblem_nvar,the number of constraints, get_nameoftheproblem_ncon, the number of linear constraints, get_nameoftheproblem_nlin, the number of nonlinear constraints, get_nameoftheproblem_nnln, the number of equality constraints, get_nameoftheproblem_nequ, and the number of inequality constraints, get_nameoftheproblem_nineq.","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"OptimizationProblems.get_AMPGO02_nvar()","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"For scalable problems the entry :variable_nvar (and/or :variable_ncon) is set as true and one can access the number of variables by passing the parameters to the getter functions. By default, the number of variables set in the meta is obtained using OptimizationProblems.default_nvar as a parameter to define the problem.","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"OptimizationProblems.arglina_meta","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"OptimizationProblems.get_arglina_nvar(n = 10)","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"OptimizationProblems.PureJuMP.arglina(n = 10)","category":"page"},{"location":"meta/#Global-meta","page":"Problem classification","title":"Global meta","text":"","category":"section"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"This package collects all the metadata in a single DataFrame.","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"OptimizationProblems.meta","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"Then, it is very simple to filter problems using queries on DataFrame. We refer to the documentation of DataFrames.jl for tutorials. For instance, if one wants to select unconstrained scalable problems and use (:nvar, :name).","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"meta = OptimizationProblems.meta\nnames_pb_vars = meta[(meta.variable_nvar .== true) .& (meta.ncon .== 0), [:nvar, :name]]","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"Then, one can prepare a list of problems using the selected ones.","category":"page"},{"location":"meta/","page":"Problem classification","title":"Problem classification","text":"using ADNLPModels\nadproblems = (\n eval(Meta.parse(\"ADNLPProblems.$(pb[:name])()\")) for pb in eachrow(names_pb_vars)\n)","category":"page"},{"location":"reference/#Reference","page":"Reference","title":"Reference","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"​","category":"page"},{"location":"reference/#Contents","page":"Reference","title":"Contents","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"​","category":"page"},{"location":"reference/","page":"Reference","title":"Reference","text":"Pages = [\"reference.md\"]","category":"page"},{"location":"reference/","page":"Reference","title":"Reference","text":"​","category":"page"},{"location":"reference/#Index","page":"Reference","title":"Index","text":"","category":"section"},{"location":"reference/","page":"Reference","title":"Reference","text":"​","category":"page"},{"location":"reference/","page":"Reference","title":"Reference","text":"Pages = [\"reference.md\"]","category":"page"},{"location":"reference/","page":"Reference","title":"Reference","text":"​","category":"page"},{"location":"reference/","page":"Reference","title":"Reference","text":"Modules = [OptimizationProblems, OptimizationProblems.PureJuMP]","category":"page"},{"location":"reference/#OptimizationProblems.meta","page":"Reference","title":"OptimizationProblems.meta","text":"OptimizationProblems.meta A composite type that represents the main features of the optimization problem. optimize obj(x) subject to lvar ≤ x ≤ uvar lcon ≤ cons(x) ≤ ucon –- The following keys are valid:\n\nnvar::Int: number of variables\nvariable_nvar::Bool: true if we can modify the number of variables\nncon::Int: number of general constraints\nvariable_ncon::Bool: true if we can modify the number of constraints\nminimize::Bool: true if optimize == minimize\nname::String: problem name\nhas_equalities_only::Bool: true if the problem has constraints, and all are equality constraints (doesn't include bounds)\nhas_inequalities_only::Bool: true if the problem has constraints, and all are inequality constraints (doesn't include bounds)\nhas_bounds::Bool: true if the problem has bound constraints\nhas_fixed_variables::Bool: true if it has fixed variables\nobjtype::Symbol: type of objective, in [:none, :constant, :linear, :quadratic, :sumofsquares, :other]\ncontype::Symbol: type of constraint, in [:unconstrained, :linear, :quadratic, :general]\nbest_known_lower_bound::Real: lower bound on the global optimal value (default: -Inf for minimization problem, f(x0) for maximization problem if x0 is feasible, -Inf otherwise)\nbest_known_upper_bound::Real: upper bound on the global optimal value (default: Inf for maximization problem, f(x0) for minimization problem if x0 is feasible, Inf otherwise)\nis_feasible::Union{Bool, Missing}: true if problem is feasible\norigin::Symbol: origin of the problem, in [:academic, :modelling, :real, :unknown]\n\n\n\n\n\n","category":"constant"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO02-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO02","text":"Univariate multimodal minimization problem AMPGO02\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO03-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO03","text":"Univariate multimodal minimization problem AMPGO03\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO04-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO04","text":"Univariate multimodal minimization problem AMPGO04\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO05-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO05","text":"Univariate multimodal minimization problem AMPGO05\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO06-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO06","text":"Univariate multimodal minimization problem AMPGO06\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO07-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO07","text":"Univariate multimodal minimization problem AMPGO07\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO08-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO08","text":"Univariate multimodal minimization problem AMPGO08\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO09-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO09","text":"Univariate multimodal minimization problem AMPGO09\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO10-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO10","text":"Univariate multimodal minimization problem AMPGO10\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO11-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO11","text":"Univariate multimodal minimization problem AMPGO11\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO12-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO12","text":"Univariate multimodal minimization problem AMPGO12\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO13-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO13","text":"Univariate multimodal minimization problem AMPGO13\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO14-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO14","text":"Univariate multimodal minimization problem AMPGO14\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO15-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO15","text":"Univariate multimodal minimization problem AMPGO15\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO18-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO18","text":"Univariate multimodal minimization problem AMPGO18\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO20-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO20","text":"Univariate multimodal minimization problem AMPGO20\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO21-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO21","text":"Univariate multimodal minimization problem AMPGO21\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.AMPGO22-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.AMPGO22","text":"Univariate multimodal minimization problem AMPGO22\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Dus2_1-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Dus2_1","text":"Univariate unimodal minimization problem Dus2_1\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Dus2_3-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Dus2_3","text":"Univariate unimodal minimization problem Dus2_3\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Dus2_9-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Dus2_9","text":"Univariate multimodal minimization problem Dus2_9\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Duscube-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Duscube","text":"Univariate multimodal minimization problem Duscube\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Shpak1-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Shpak1","text":"Univariate multimodal minimization problem Shpak1\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Shpak2-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Shpak2","text":"Univariate multimodal minimization problem Shpak2\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Shpak3-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Shpak3","text":"Univariate multimodal minimization problem Shpak3\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Shpak4-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Shpak4","text":"Univariate multimodal minimization problem Shpak4\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Shpak5-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Shpak5","text":"Univariate multimodal minimization problem Shpak5\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.Shpak6-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.Shpak6","text":"Univariate multimodal minimization problem Shpak6\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.arglina-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.arglina","text":"Linear function with n parameters and m observations - full rank\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.arglinb-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.arglinb","text":"Linear function with n parameters and m observations - rank 1\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.arglinc-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.arglinc","text":"Linear function with n parameters and m observations - rank 1, zero columns and rows\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.argtrig-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.argtrig","text":"Linear function with n parameters and m observations - full rank\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.arwhead-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.arwhead","text":"Arrow head model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.bard-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.bard","text":"Linear function with n parameters and m observations - full rank\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.bdqrtic-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.bdqrtic","text":"Banded quartic model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.beale-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.beale","text":"Beale Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.brownal-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.brownal","text":"Brownbs Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.brownbs-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.brownbs","text":"Brownbs Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.brownden-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.brownden","text":"Brown and Dennis function\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.broydn7d-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.broydn7d","text":"Broyden 7-diagonal model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.brybnd-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.brybnd","text":"Broyden banded model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.bt1-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.bt1","text":"BT1 Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.chainwoo-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.chainwoo","text":"The chained Woods function in size n, a variant on the Woods function\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.cliff-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.cliff","text":"The 'cliff problem' in 2 variables\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.clnlbeam-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.clnlbeam","text":"The clnlbeam problem in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.clplatea-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.clplatea","text":"The clamped plate problem (Strang, Nocedal, Dax).\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.clplateb-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.clplateb","text":"The clamped plate problem (Strang, Nocedal, Dax).\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.clplatec-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.clplatec","text":"The clamped plate problem (Strang, Nocedal, Dax).\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.cosine-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.cosine","text":"The cosine function in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.cragglvy-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.cragglvy","text":"The extented Cragg and Levy function in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.cragglvy2-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.cragglvy2","text":"The extented Cragg and Levy function in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.curly-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.curly","text":"Curly function in size n with semi-bandwidth b\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.curly10-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.curly10","text":"Curly function in size n with semi-bandwidth 10\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.curly20-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.curly20","text":"Curly function in size n with semi-bandwidth 20\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.curly30-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.curly30","text":"Curly function in size n with semi-bandwidth 30\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaane-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaane","text":"Dixon-Maany function in size n (version E by default)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaanf-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaanf","text":"Dixon-Maany function in size n (version F)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaang-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaang","text":"Dixon-Maany function in size n (version G)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaanh-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaanh","text":"Dixon-Maany function in size n (version H)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaani-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaani","text":"Dixon-Maany function in size n (version I by default)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaanj-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaanj","text":"Dixon-Maany function in size n (version J)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaank-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaank","text":"Dixon-Maany function in size n (version K)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaanl-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaanl","text":"Dixon-Maany function in size n (version L)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaanm-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaanm","text":"Dixon-Maany function in size n (version M by default)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaann-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaann","text":"Dixon-Maany function in size n (version N)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaano-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaano","text":"Dixon-Maany function in size n (version O)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixmaanp-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixmaanp","text":"Dixon-Maany function in size n (version P)\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dixon3dq-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dixon3dq","text":"Dixon's tridiagonal quadratic.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dqdrtic-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dqdrtic","text":"Diagonal quadratic problem\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.dqrtic-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.dqrtic","text":"Diagonal quartic model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.edensch-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.edensch","text":"Extended Dennis-Schnabel model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.eg2-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.eg2","text":"model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.engval1-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.engval1","text":"The Engval1 model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.genrose-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.genrose","text":"Generalized Rosenbrock model in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.genrose_nash-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.genrose_nash","text":"Nash's variant of genrose() in size n\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.gulf-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.gulf","text":"Linear function with n parameters and m observations - full rank\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs1-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs1","text":"HS1 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs10-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs10","text":"HS10 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs100-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs100","text":"HS100 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs101-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs101","text":"HS101 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs102-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs102","text":"HS102 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs103-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs103","text":"HS103 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs104-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs104","text":"HS104 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs105-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs105","text":"HS105 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs106-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs106","text":"HS106 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs107-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs107","text":"HS107 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs108-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs108","text":"HS108 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs109-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs109","text":"HS109 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs11-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs11","text":"HS11 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs110-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs110","text":"HS110 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs111-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs111","text":"HS111 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs112-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs112","text":"HS112 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs113-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs113","text":"HS113 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs114-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs114","text":"HS114 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs116-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs116","text":"HS116 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs117-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs117","text":"HS117 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs118-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs118","text":"HS118 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs119-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs119","text":"HS119 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs12-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs12","text":"HS12 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs13-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs13","text":"HS13 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs14-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs14","text":"HS14 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs15-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs15","text":"HS15 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs16-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs16","text":"HS16 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs17-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs17","text":"HS17 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs18-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs18","text":"HS18 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs19-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs19","text":"HS19 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs2-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs2","text":"HS2 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs20-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs20","text":"HS20 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs201-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs201","text":"HS201 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs21-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs21","text":"HS21 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs211-Tuple{}","page":"Reference","title":"OptimizationProblems.PureJuMP.hs211","text":"HS211 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs22-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs22","text":"HS22 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs220-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs220","text":"HS220 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs221-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs221","text":"HS221 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs222-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs222","text":"HS222 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs223-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs223","text":"HS223 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs224-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs224","text":"HS224 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs225-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs225","text":"HS225 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs226-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs226","text":"HS226 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs227-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs227","text":"HS227 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs228-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs228","text":"HS228 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs229-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs229","text":"HS229 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs23-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs23","text":"HS23 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs230-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs230","text":"HS230 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs231-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs231","text":"HS231 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs232-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs232","text":"HS232 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs233-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs233","text":"HS233 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs234-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs234","text":"HS234 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs235-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs235","text":"HS235 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs236-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs236","text":"HS236 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs237-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs237","text":"HS237 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs238-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs238","text":"HS238 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs239-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs239","text":"HS239 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs24-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs24","text":"HS24 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs240-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs240","text":"HS240 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs241-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs241","text":"HS241 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs242-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs242","text":"HS242 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs243-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs243","text":"HS243 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs244-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs244","text":"HS244 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs245-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs245","text":"HS245 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs246-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs246","text":"HS246 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs248-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs248","text":"HS248 model: around the world problem\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs249-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs249","text":"HS249 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs25-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs25","text":"HS25 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs250-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs250","text":"HS250 model: Rosenbrock's post office problem\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs251-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs251","text":"HS251 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs252-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs252","text":"HS252 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs253-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs253","text":"HS253 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs254-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs254","text":"HS254 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs255-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs255","text":"HS255 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs256-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs256","text":"HS256 model: Powell's function\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs257-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs257","text":"HS257 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs258-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs258","text":"HS258 model: Powell's function\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs259-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs259","text":"HS259 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs26-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs26","text":"HS26 Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs260-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs260","text":"HS260 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs261-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs261","text":"HS261 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs262-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs262","text":"HS262 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs263-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs263","text":"HS263 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs264-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs264","text":"HS264 model: modified Rosen-Suzuki problem\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs265-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs265","text":"HS265 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs27-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs27","text":"HS27 Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs28-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs28","text":"HS28 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs29-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs29","text":"HS29 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs3-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs3","text":"HS3 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs30-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs30","text":"HS30 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs31-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs31","text":"HS31 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs316-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs316","text":"HS316 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs317-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs317","text":"HS317 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs318-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs318","text":"HS318 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs319-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs319","text":"HS319 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs32-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs32","text":"HS32 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs320-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs320","text":"HS320 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs321-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs321","text":"HS321 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs322-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs322","text":"HS322 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs33-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs33","text":"HS33 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs34-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs34","text":"HS34 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs35-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs35","text":"HS35 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs36-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs36","text":"HS36 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs37-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs37","text":"HS37 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs378-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs378","text":"HS378 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs38-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs38","text":"HS38 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs39-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs39","text":"HS39 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs4-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs4","text":"HS4 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs40-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs40","text":"HS40 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs41-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs41","text":"HS41 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs42-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs42","text":"HS42 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs43-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs43","text":"HS43 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs44-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs44","text":"HS44 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs45-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs45","text":"HS45 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs46-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs46","text":"HS46 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs47-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs47","text":"HS47 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs48-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs48","text":"HS48 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs49-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs49","text":"HS49 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs5-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs5","text":"HS5 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs50-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs50","text":"HS50 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs51-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs51","text":"HS51 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs52-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs52","text":"HS52 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs53-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs53","text":"HS53 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs54-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs54","text":"HS54 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs55-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs55","text":"HS55 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs56-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs56","text":"HS56 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs57-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs57","text":"HS57 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs59-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs59","text":"HS59 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs6-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs6","text":"HS6 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs60-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs60","text":"HS60 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs61-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs61","text":"HS61 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs62-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs62","text":"HS62 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs63-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs63","text":"HS63 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs64-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs64","text":"HS64 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs65-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs65","text":"HS65 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs66-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs66","text":"HS66 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs7-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs7","text":"HS7 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs70-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs70","text":"HS70 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs71-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs71","text":"HS71 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs72-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs72","text":"HS72 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs73-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs73","text":"HS73 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs74-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs74","text":"HS74 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs75-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs75","text":"HS75 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs76-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs76","text":"HS76 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs77-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs77","text":"HS77 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs78-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs78","text":"HS78 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs79-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs79","text":"HS79 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs8-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs8","text":"HS8 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs80-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs80","text":"HS80 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs81-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs81","text":"HS81 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs83-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs83","text":"HS83 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs84-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs84","text":"HS84 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs86-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs86","text":"HS86 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs87-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs87","text":"HS87 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs9-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs9","text":"HS9 Model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs93-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs93","text":"HS93 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs95-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs95","text":"HS95 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs96-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs96","text":"HS96 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs97-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs97","text":"HS97 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs98-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs98","text":"HS98 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.hs99-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.hs99","text":"HS99 model\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.integreq-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.integreq","text":"Linear function with n parameters and m observations - full rank\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.meyer3-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.meyer3","text":"Meyer function\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.nasty-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.nasty","text":"Nasty problem\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer1c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer1c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer1d-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer1d","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer2c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer2c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer3c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer3c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer4c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer4c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer5c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer5c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer5d-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer5d","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer6c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer6c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer7c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer7c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.palmer8c-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.palmer8c","text":"A linear least squares problem arising from chemical kinetics.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.penalty1-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.penalty1","text":"Linear function with n parameters and m observations - full rank\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.penalty2-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.penalty2","text":"A penalty problem by Gill, Murray and Pitfield in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.penalty3-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.penalty3","text":"A penalty problem by Gill, Murray and Pitfield in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.powellsg-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.powellsg","text":"The extended Powell singular problem in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.power-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.power","text":"The Power problem by Oren.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.quartc-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.quartc","text":"A simple quartic function.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.rosenbrock-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.rosenbrock","text":"Classic Rosenbrock problem\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.sbrybnd-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.sbrybnd","text":"Broyden banded system of nonlinear equations in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.schmvett-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.schmvett","text":"Another function with nontrivial groups and repetitious elements in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.scosine-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.scosine","text":"Another function with nontrivial groups and repetitious elements in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.sinquad-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.sinquad","text":"Another function with nontrivial groups and repetitious elements in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.sparsine-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.sparsine","text":"A sparse problem involving sine functions in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.sparsqur-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.sparsqur","text":"A sparse quartic problem in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.srosenbr-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.srosenbr","text":"The separable extension of Rosenbrock's function 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.tointgss-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.tointgss","text":"Toint's Gaussian problem in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.tquartic-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.tquartic","text":"A quartic function with nontrivial groups and repetitious elements in size 'n' \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.tridia-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.tridia","text":"Shanno's TRIDIA quadratic tridiagonal problem.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.vardim-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.vardim","text":"Variable dimension problem.\n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.woods-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.woods","text":"The extended Woods problem n \n\n\n\n\n\n","category":"method"},{"location":"reference/#OptimizationProblems.PureJuMP.zangwil3-Tuple","page":"Reference","title":"OptimizationProblems.PureJuMP.zangwil3","text":"Zangwil3 Model\n\n\n\n\n\n","category":"method"},{"location":"benchmark/#Run-a-benchmark-with-OptimizationProblems.jl","page":"Benchmark","title":"Run a benchmark with OptimizationProblems.jl","text":"","category":"section"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"In this more advanced tutorial, we use the problems from OptimizationProblems to run a benchmark for unconstrained problems. The tutorial will use:","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"JSOSolvers: This package provides optimization solvers in pure Julia for unconstrained and bound-constrained optimization.\nNLPModelsJuMP: This package convert JuMP model in NLPModel format.\nSolverBenchmark: This package provides general tools for benchmarking solvers.","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"using JSOSolvers, NLPModels, NLPModelsJuMP, OptimizationProblems, SolverBenchmark\nusing OptimizationProblems.PureJuMP","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"We select the problems from PureJuMP submodule of OptimizationProblems converted in NLPModels using NLPModelsJuMP.","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"problems = (MathOptNLPModel(OptimizationProblems.PureJuMP.eval(Meta.parse(problem))(), name=problem) for problem ∈ OptimizationProblems.meta[!, :name])","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"The same can be achieved using OptimizationProblems.ADNLPProblems instead of OptimizationProblems.PureJuMP as follows:","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"using ADNLPModels\nusing OptimizationProblems.ADNLPProblems\nad_problems = (OptimizationProblems.ADNLPProblems.eval(Meta.parse(problem))() for problem ∈ OptimizationProblems.meta[!, :name])","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"We also define a dictionary of solvers that will be used for our benchmark. We consider here JSOSolvers.lbfgs and JSOSolvers.trunk.","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"solvers = Dict(\n :lbfgs => model -> lbfgs(model, mem=5, atol=1e-5, rtol=0.0),\n :trunk => model -> trunk(model, atol=1e-5, rtol=0.0),\n)","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"The function SolverBenchmark.bmak_solvers will run all the problems on the specified solvers and store the results in a DataFrame. At this stage, we discard the problems that have constraints or bounds using !unconstrained(prob), and those that are too large or too small with get_nvar(prob) > 100 || get_nvar(prob) < 5.","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"stats = bmark_solvers(\n solvers, problems,\n skipif=prob -> (!unconstrained(prob) || get_nvar(prob) > 100 || get_nvar(prob) < 5),\n)","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"We can explore the results solver by solver in stats[:lbfgs] and stats[:trunk], or get a profile wall using SolverBenchmark.profile_solvers.","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"cols = [:id, :name, :nvar, :objective, :dual_feas, :neval_obj, :neval_grad, :neval_hess, :iter, :elapsed_time, :status]\nheader = Dict(\n :nvar => \"n\",\n :objective => \"f(x)\",\n :dual_feas => \"‖∇f(x)‖\",\n :neval_obj => \"# f\",\n :neval_grad => \"# ∇f\",\n :neval_hess => \"# ∇²f\",\n :elapsed_time => \"t\",\n)\n\nfor solver ∈ keys(solvers)\n pretty_stats(stats[solver][!, cols], hdr_override=header)\nend","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"first_order(df) = df.status .== :first_order\nunbounded(df) = df.status .== :unbounded\nsolved(df) = first_order(df) .| unbounded(df)\ncostnames = [\"time\", \"obj + grad + hess\"]\ncosts = [\n df -> .!solved(df) .* Inf .+ df.elapsed_time,\n df -> .!solved(df) .* Inf .+ df.neval_obj .+ df.neval_grad .+ df.neval_hess,\n]\n\nusing Plots\ngr()\n\nprofile_solvers(stats, costs, costnames)","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"It is also possible to select problems when initializing the problem list by filtering OptimizationProblems.meta:","category":"page"},{"location":"benchmark/","page":"Benchmark","title":"Benchmark","text":"meta = OptimizationProblems.meta\nproblem_list = meta[(meta.ncon .== 0) .& .!meta.has_bounds .& (5 .<= meta.nvar .<= 100), :name]\nproblems = (MathOptNLPModel(eval(Meta.parse(problem))(), name=problem) for problem ∈ problem_list)","category":"page"},{"location":"#OptimizationProblems.jl","page":"Home","title":"OptimizationProblems.jl","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"This package provides a collection of optimization problems in JuMP and ADNLPModels syntax.","category":"page"},{"location":"#Installing","page":"Home","title":"Installing","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"OptimizationProblems can be installed and tested through the Julia package manager:","category":"page"},{"location":"","page":"Home","title":"Home","text":"julia> ]\npkg> add OptimizationProblems\npkg> test OptimizationProblems","category":"page"},{"location":"#How-to-cite","page":"Home","title":"How to cite","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"If you use OptimizationProblems.jl in your work, please cite using the format given in CITATION.cff.","category":"page"},{"location":"#Bug-reports-and-discussions","page":"Home","title":"Bug reports and discussions","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"If you think you found a bug, feel free to open an issue. Focused suggestions and requests can also be opened as issues. Before opening a pull request, start an issue or a discussion on the topic, please.","category":"page"},{"location":"","page":"Home","title":"Home","text":"If you want to ask a question not suited for a bug report, feel free to start a discussion here. This forum is for general discussion about this repository and the JuliaSmoothOptimizers, so questions about any of our packages are welcome.","category":"page"},{"location":"tutorial/#OptimizationProblems.jl-Tutorial","page":"Tutorial","title":"OptimizationProblems.jl Tutorial","text":"","category":"section"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"In this tutorial, we will see how to access the problems in JuMP and ADNLPModel syntax. This package is subdivided in two submodules: PureJuMP for the JuMP problems, ADNLPProblems for the ADNLPModel problems.","category":"page"},{"location":"tutorial/#Problems-in-JuMP-syntax:-PureJuMP","page":"Tutorial","title":"Problems in JuMP syntax: PureJuMP","text":"","category":"section"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"You can obtain the list of problems currently defined with OptimizationProblems.meta[!, :name].","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"using OptimizationProblems, OptimizationProblems.PureJuMP\nproblems = OptimizationProblems.meta[!, :name]\nlength(problems)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"Then, it suffices to select any of this problem to get the JuMP model.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"jump_model = zangwil3()","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"Note that certain problems are scalable, i.e., their size depends on parameters that can be modified. The list of those problems is available once again using meta:","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"var_problems = OptimizationProblems.meta[OptimizationProblems.meta.variable_nvar, :name]\nlength(var_problems)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"Then, using the keyword n, it is possible to specify the targeted number of variables.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"jump_model_12 = woods(n=12)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"jump_model_120 = woods(n=120)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"These problems can be converted as NLPModels via NLPModelsJuMP to facilitate evaluating objective, constraints and their derivatives.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"using NLPModels, NLPModelsJuMP\nnlp_model_120 = MathOptNLPModel(jump_model_120)\nobj(nlp_model_120, zeros(120))","category":"page"},{"location":"tutorial/#Problems-in-ADNLPModel-syntax:-ADNLPProblems","page":"Tutorial","title":"Problems in ADNLPModel syntax: ADNLPProblems","text":"","category":"section"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"This package also offers ADNLPModel test problems. This is an optional dependency, so ADNLPModels has to be added first.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"using ADNLPModels","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"You can obtain the list of problems currently defined with OptimizationProblems.meta[!, :name]:","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"using OptimizationProblems, OptimizationProblems.ADNLPProblems\nproblems = OptimizationProblems.meta[!, :name]\nlength(problems)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"Similarly, to the PureJuMP models, it suffices to select any of this problem to get the model.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"nlp = zangwil3()","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"Note that some of these problems are scalable, i.e., their size depends on some parameters that can be modified.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"nlp_12 = woods(n=12)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"nlp_120 = woods(n=120)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"One of the advantages of these problems is that they are type-stable. Indeed, one can specify the output type with the keyword type as follows.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"nlp16_12 = woods(n=12, type=Float16)","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"Then, all the API will be compatible with the precised type.","category":"page"},{"location":"tutorial/","page":"Tutorial","title":"Tutorial","text":"using NLPModels\nobj(nlp16_12, zeros(Float16, 12))","category":"page"}] } diff --git a/dev/tutorial/index.html b/dev/tutorial/index.html index 9eb7be1c..c29353b6 100644 --- a/dev/tutorial/index.html +++ b/dev/tutorial/index.html @@ -1,7 +1,7 @@ Tutorial · OptimizationProblems.jl
GitHub

OptimizationProblems.jl Tutorial

In this tutorial, we will see how to access the problems in JuMP and ADNLPModel syntax. This package is subdivided in two submodules: PureJuMP for the JuMP problems, ADNLPProblems for the ADNLPModel problems.

Problems in JuMP syntax: PureJuMP

You can obtain the list of problems currently defined with OptimizationProblems.meta[!, :name].

using OptimizationProblems, OptimizationProblems.PureJuMP
 problems = OptimizationProblems.meta[!, :name]
-length(problems)
373

Then, it suffices to select any of this problem to get the JuMP model.

jump_model = zangwil3()
A JuMP Model
+length(problems)
374

Then, it suffices to select any of this problem to get the JuMP model.

jump_model = zangwil3()
A JuMP Model
 ├ solver: none
 ├ objective_sense: MIN_SENSE
 │ └ objective_function_type: JuMP.AffExpr
@@ -28,7 +28,7 @@
 nlp_model_120 = MathOptNLPModel(jump_model_120)
 obj(nlp_model_120, zeros(120))
1260.0

Problems in ADNLPModel syntax: ADNLPProblems

This package also offers ADNLPModel test problems. This is an optional dependency, so ADNLPModels has to be added first.

using ADNLPModels

You can obtain the list of problems currently defined with OptimizationProblems.meta[!, :name]:

using OptimizationProblems, OptimizationProblems.ADNLPProblems
 problems = OptimizationProblems.meta[!, :name]
-length(problems)
373

Similarly, to the PureJuMP models, it suffices to select any of this problem to get the model.

nlp = zangwil3()
ADNLPModel - Model with automatic differentiation backend ADModelBackend{
+length(problems)
374

Similarly, to the PureJuMP models, it suffices to select any of this problem to get the model.

nlp = zangwil3()
ADNLPModel - Model with automatic differentiation backend ADModelBackend{
   ForwardDiffADGradient,
   ForwardDiffADHvprod,
   ForwardDiffADJprod,
@@ -145,4 +145,4 @@
       jtprod_nln: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 hess: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                hprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
            jhess: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0               jhprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0

Then, all the API will be compatible with the precised type.

using NLPModels
-obj(nlp16_12, zeros(Float16, 12))
Float16(126.0)
+obj(nlp16_12, zeros(Float16, 12))
Float16(126.0)