🤖 Format .jl files (#254)

Co-authored-by: amontoison <[email protected]>
JuliaSmoothOptimizers · Jun 22, 2024 · 07c6afb · 07c6afb
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Showing 6 changed files with 19 additions and 27 deletions.
diff --git a/src/ad_api.jl b/src/ad_api.jl
@@ -139,7 +139,8 @@ end
 
 function get_residual_nnzh(
   b::ADModelBackend{GB, HvB, JvB, JtvB, JB, HB, GHJ, HvBLS, JvBLS, JtvBLS, JBLS, HBLS},
-  nvar) where {GB, HvB, JvB, JtvB, JB, HB, GHJ, HvBLS, JvBLS, JtvBLS, JBLS, HBLS <: AbstractNLPModel}
+  nvar,
+) where {GB, HvB, JvB, JtvB, JB, HB, GHJ, HvBLS, JvBLS, JtvBLS, JBLS, HBLS <: AbstractNLPModel}
   nls = b.hessian_residual_backend
   nls.nls_meta.nnzh
 end

diff --git a/src/nls.jl b/src/nls.jl
@@ -166,8 +166,7 @@ function ADNLSModel!(
   meta = NLPModelMeta{T, S}(nvar, x0 = x0, nnzh = nnzh, name = name, minimize = minimize)
   nls_nnzj = get_residual_nnzj(adbackend, nvar, nequ)
   nls_nnzh = get_residual_nnzh(adbackend, nvar)
-  nls_meta =
-    NLSMeta{T, S}(nequ, nvar, nnzj = nls_nnzj, nnzh = nls_nnzh, lin = linequ)
+  nls_meta = NLSMeta{T, S}(nequ, nvar, nnzj = nls_nnzj, nnzh = nls_nnzh, lin = linequ)
   return ADNLSModel(meta, nls_meta, NLSCounters(), adbackend, F!, (cx, x) -> cx)
 end
 
@@ -212,8 +211,7 @@ function ADNLSModel!(
   )
   nls_nnzj = get_residual_nnzj(adbackend, nvar, nequ)
   nls_nnzh = get_residual_nnzh(adbackend, nvar)
-  nls_meta =
-    NLSMeta{T, S}(nequ, nvar, nnzj = nls_nnzj, nnzh = nls_nnzh, lin = linequ)
+  nls_meta = NLSMeta{T, S}(nequ, nvar, nnzj = nls_nnzj, nnzh = nls_nnzh, lin = linequ)
   return ADNLSModel(meta, nls_meta, NLSCounters(), adbackend, F!, (cx, x) -> cx)
 end
 
@@ -275,8 +273,7 @@ function ADNLSModel!(
   )
   nls_nnzj = get_residual_nnzj(adbackend, nvar, nequ)
   nls_nnzh = get_residual_nnzh(adbackend, nvar)
-  nls_meta =
-    NLSMeta{T, S}(nequ, nvar, nnzj = nls_nnzj, nnzh = nls_nnzh, lin = linequ)
+  nls_meta = NLSMeta{T, S}(nequ, nvar, nnzj = nls_nnzj, nnzh = nls_nnzh, lin = linequ)
   return ADNLSModel(meta, nls_meta, NLSCounters(), adbackend, F!, c!)
 end
 
@@ -426,8 +423,7 @@ function ADNLSModel!(
   )
   nls_nnzj = get_residual_nnzj(adbackend, nvar, nequ)
   nls_nnzh = get_residual_nnzh(adbackend, nvar)
-  nls_meta =
-    NLSMeta{T, S}(nequ, nvar, nnzj = nls_nnzj, nnzh = nls_nnzh, lin = linequ)
+  nls_meta = NLSMeta{T, S}(nequ, nvar, nnzj = nls_nnzj, nnzh = nls_nnzh, lin = linequ)
   return ADNLSModel(meta, nls_meta, NLSCounters(), adbackend, F!, clinrows, clincols, clinvals, c!)
 end
 
@@ -644,8 +640,7 @@ function ADNLSModel!(
   )
   nls_nnzj = get_residual_nnzj(adbackend, nvar, nequ)
   nls_nnzh = get_residual_nnzh(adbackend, nvar)
-  nls_meta =
-    NLSMeta{T, S}(nequ, nvar, nnzj = nls_nnzj, nnzh = nls_nnzh, lin = linequ)
+  nls_meta = NLSMeta{T, S}(nequ, nvar, nnzj = nls_nnzj, nnzh = nls_nnzh, lin = linequ)
   return ADNLSModel(meta, nls_meta, NLSCounters(), adbackend, F!, c!)
 end
 
@@ -750,8 +745,7 @@ function ADNLSModel!(
   )
   nls_nnzj = get_residual_nnzj(adbackend, nvar, nequ)
   nls_nnzh = get_residual_nnzh(adbackend, nvar)
-  nls_meta =
-    NLSMeta{T, S}(nequ, nvar, nnzj = nls_nnzj, nnzh = nls_nnzh, lin = linequ)
+  nls_meta = NLSMeta{T, S}(nequ, nvar, nnzj = nls_nnzj, nnzh = nls_nnzh, lin = linequ)
   return ADNLSModel(meta, nls_meta, NLSCounters(), adbackend, F!, clinrows, clincols, clinvals, c!)
 end
 

diff --git a/test/sparse_hessian.jl b/test/sparse_hessian.jl
@@ -1,7 +1,8 @@
-list_sparse_hess_backend =
-  ((ADNLPModels.SparseADHessian, Dict()),
-   (ADNLPModels.SparseReverseADHessian, Dict()),
-   (ADNLPModels.ForwardDiffADHessian, Dict()))
+list_sparse_hess_backend = (
+  (ADNLPModels.SparseADHessian, Dict()),
+  (ADNLPModels.SparseReverseADHessian, Dict()),
+  (ADNLPModels.ForwardDiffADHessian, Dict()),
+)
 
 dt = (Float32, Float64)
 

diff --git a/test/sparse_hessian_nls.jl b/test/sparse_hessian_nls.jl
@@ -28,7 +28,7 @@ dt = (Float32, Float64)
   hess_coord_residual!(nls, x, v, vals)
   @test eltype(vals) == T
   H = Symmetric(sparse(rows, cols, vals, nvar, nvar), :L)
-  @test H == [-20 * v[2] 0; 0 0]
+  @test H == [-20*v[2] 0; 0 0]
 
   # Test also the implementation of the backends
   b = nls.adbackend.hessian_residual_backend
@@ -37,7 +37,7 @@ dt = (Float32, Float64)
   ADNLPModels.hess_coord_residual!(b, nls, x, v, vals)
   @test eltype(vals) == T
   H = Symmetric(sparse(rows, cols, vals, nvar, nvar), :L)
-  @test H == [-20 * v[2] 0; 0 0]
+  @test H == [-20*v[2] 0; 0 0]
 
   nls = ADNLPModels.ADNLSModel!(F!, x0, 3, matrix_free = true; kw...)
   @test nls.adbackend.hessian_backend isa ADNLPModels.EmptyADbackend

diff --git a/test/sparse_jacobian.jl b/test/sparse_jacobian.jl
@@ -1,7 +1,5 @@
-list_sparse_jac_backend = (
-  (ADNLPModels.SparseADJacobian, Dict()),
-  (ADNLPModels.ForwardDiffADJacobian, Dict()),
-)
+list_sparse_jac_backend =
+  ((ADNLPModels.SparseADJacobian, Dict()), (ADNLPModels.ForwardDiffADJacobian, Dict()))
 
 dt = (Float32, Float64)
 

diff --git a/test/sparse_jacobian_nls.jl b/test/sparse_jacobian_nls.jl
@@ -1,7 +1,5 @@
-list_sparse_jac_backend = (
-  (ADNLPModels.SparseADJacobian, Dict()),
-  (ADNLPModels.ForwardDiffADJacobian, Dict()),
-)
+list_sparse_jac_backend =
+  ((ADNLPModels.SparseADJacobian, Dict()), (ADNLPModels.ForwardDiffADJacobian, Dict()))
 
 dt = (Float32, Float64)