diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 84b5c00..bd6e174 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.11.0","generation_timestamp":"2024-10-11T23:45:41","documenter_version":"1.7.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.11.1","generation_timestamp":"2024-11-02T14:41:20","documenter_version":"1.7.0"}} \ No newline at end of file diff --git a/dev/classification/index.html b/dev/classification/index.html index 6047d95..cc321aa 100644 --- a/dev/classification/index.html +++ b/dev/classification/index.html @@ -5,4 +5,4 @@ only_nonlinear_con=false, only_equ_con=false, only_ineq_con=false, custom_filter=*)

Returns a subset of the CUTEst problems using the classification file classf.json.

Keyword arguments

filtered_problems1 = select_sif_problems(; min_var=10, max_var=100, only_linear_con=true)
 filtered_problems2 = select_sif_problems(; max_con=0)
-filtered_problems3 = select_sif_problems(; min_con=1)
source
CUTEst.build_classification — Function
build_classification()

Creates the file classf.json, running each problem in CLASSF.DB and extracting the necessary information. It should be left alone, unless you think it is not updated. If you do, please open an issue.

source
+filtered_problems3 = select_sif_problems(; min_con=1)source
CUTEst.build_classification — Function
build_classification()

Creates the file classf.json, running each problem in CLASSF.DB and extracting the necessary information. It should be left alone, unless you think it is not updated. If you do, please open an issue.

source
diff --git a/dev/core/index.html b/dev/core/index.html index 31f5590..bc1cc5c 100644 --- a/dev/core/index.html +++ b/dev/core/index.html @@ -7,4 +7,4 @@ x = rand(T, nlp.meta.nvar) f = Ref{T}() CUTEst.ufn(T, nlp.libsif, nlp.status, nlp.nvar, x, f) -println(f[])
CUTEst.cjprod — Function
cjprod(T, libsif, status, n, m, gotj, jtrans, x, vector, lvector, result, lresult)

The cjprod subroutine forms the product of a vector with the Jacobian matrix, or with its transpose, of the constraint functions of the problem decoded from a SIF file by the script sifdecoder evaluated at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • gotj: [IN] Vector{Bool}
  • jtrans: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • vector: [IN] Vector{T}
  • lvector: [IN] Vector{Cint}
  • result: [OUT] Vector{T}
  • lresult: [IN] Vector{Cint}
source
CUTEst.ccfg — Function
ccfg(T, libsif, status, n, m, x, c, jtrans, lcjac1, lcjac2, cjac, grad)

The ccfg subroutine evaluates the values of the constraint functions of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly their gradients. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • c: [OUT] Vector{T}
  • jtrans: [IN] Vector{Bool}
  • lcjac1: [IN] Vector{Cint}
  • lcjac2: [IN] Vector{Cint}
  • cjac: [OUT] Matrix{T}
  • grad: [IN] Vector{Bool}
source
CUTEst.ccf — Function
ccf(T, libsif, status, n, m, x, c)
source
CUTEst.cish — Function
cish(T, libsif, status, n, x, iprob, nnzh, lh, h_val, h_row, h_col)

The cish subroutine evaluates the Hessian of a particular constraint function or the objective function for the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient. The matrix is stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • iprob: [IN] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_val: [OUT] Vector{T}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.ushprod — Function
ushprod(T, libsif, status, n, goth, x, nnz_vector, index_nz_vector, vector, nnz_result, index_nz_result, result)

The ushprod subroutine forms the product of a sparse vector with the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • goth: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • nnz_vector: [IN] Vector{Cint}
  • indexnzvector: [IN] Vector{Cint}
  • vector: [IN] Vector{T}
  • nnz_result: [OUT] Vector{Cint}
  • indexnzresult: [OUT] Vector{Cint}
  • result: [OUT] Vector{T}

Notice that vector and result should have allocated dimension of n.

source
CUTEst.cdimchp — Function
cdimchp(T, libsif, status, nnzchp)

The cdimchp subroutine determines the number of nonzero elements required to store the products of the Hessian matrices of the constraint functions with a specified vector for the problem decoded into OUTSDIF.d in the constrained minimization case. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • nnzchp: [OUT] Vector{Cint}
source
CUTEst.ureport — Function
ureport(T, libsif, status, calls, time)

The ureport subroutine obtains statistics concerning function evaluation and CPU time used for unconstrained or bound-constrained optimization in a standardized format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group- partially separable.

  • status: [OUT] Vector{Cint}
  • calls: [OUT] Vector{T}
  • time: [OUT] Vector{T}
source
CUTEst.chcprod — Function
chcprod(T, libsif, status, n, m, goth, x, y, vector, result)

The chcprod subroutine forms the product of a vector with the Hessian matrix of the constraint part of the Lagrangian function yTc(x) of the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • goth: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • vector: [IN] Vector{T}
  • result: [OUT] Vector{T}
source
CUTEst.udimse — Function
udimse(T, libsif, status, ne, he_val_ne, he_row_ne)

The udimse subroutine determine the number of nonzeros required to store the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a sparse matrix in finite element format H=eΣ1He, where each square symmetric element H_i involves a small subset of the rows of the Hessian matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • ne: [OUT] Vector{Cint}
  • hevalne: [OUT] Vector{Cint}
  • herowne: [OUT] Vector{Cint}
source
CUTEst.ubandh — Function
ubandh(T, libsif, status, n, x, semibandwidth, h_band, lbandh, max_semibandwidth)

The ubandh subroutine extracts the elements which lie within a band of given semi-bandwidth out of the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group- partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • semibandwidth: [IN] Vector{Cint}
  • h_band: [OUT] Matrix{T}
  • lbandh: [IN] Vector{Cint}
  • max_semibandwidth: [OUT] Vector{Cint}
source
CUTEst.csjp — Function
csjp(T, libsif, status, nnzj, lj, jvar, jcon)

The csjp subroutine evaluates the sparsity pattern of the Jacobian of the constraints for a problem decoded from a SIF file by the script sifdecoder.

  • status: [OUT] Vector{Cint}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • jvar: [OUT] Vector{Cint}
  • jcon: [OUT] Vector{Cint}
source
CUTEst.cgr — Function
cgr(T, libsif, status, n, m, x, y, grlagf, g, jtrans, lj1, lj2, j_val)

The cgr subroutine evaluates the gradients of the general constraints and of either the objective function f(x) or the Lagrangian function l(x,y)=f(x)+yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • grlagf: [IN] Vector{Bool}
  • g: [OUT] Vector{T}
  • jtrans: [IN] Vector{Bool}
  • lj1: [IN] Vector{Cint}
  • lj2: [IN] Vector{Cint}
  • j_val: [OUT] Matrix{T}
source
CUTEst.ufn — Function
ufn(T, libsif, status, n, x, f)

The ufn subroutine evaluates the value of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • f: [OUT] Vector{T}
source
CUTEst.csgrp — Function
csgrp(T, libsif, status, n, nnzj, lj, j_var, j_fun)

The csgrp subroutine evaluates sparsity pattern used when storing the gradients of the general constraints and of either the objective function or the Lagrangian function l(x,y)=f(x)+yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • j_var: [OUT] Vector{Cint}
  • j_fun: [OUT] Vector{Cint}
source
CUTEst.cshc — Function
cshc(T, libsif, status, n, m, x, y, nnzh, lh, h_val, h_row, h_col)

The cshc subroutine evaluates the Hessian matrix of the constraint part of the Lagrangian function yTc(x) for the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The matrix is stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_val: [OUT] Vector{T}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.cdimsj — Function
cdimsj(T, libsif, status, nnzj)

The cdimsj subroutine determines the number of nonzero elements required to store the matrix of gradients of the objective function and constraint functions for the problem decoded into OUTSDIF.d in the constrained minimization case. The matrix is stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • nnzj: [OUT] Vector{Cint}
source
CUTEst.cfn — Function
cfn(T, libsif, status, n, m, x, f, c)

The cfn subroutine evaluates the value of the objective function and general constraint functions of the problem decoded from a SIF file by the script sifdecoder at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • f: [OUT] Vector{T}
  • c: [OUT] Vector{T}
source
CUTEst.cofg — Function
cofg(T, libsif, status, n, x, f, g, grad)

The cofg subroutine evaluates the value of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • f: [OUT] Vector{T}
  • g: [OUT] Vector{T}
  • grad: [IN] Vector{Bool}
source
CUTEst.cofsg — Function
cofsg(T, libsif, status, n, x, f, nnzg, lg, g_val, g_var, grad)

The cofsg subroutine evaluates the value of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • f: [OUT] Vector{T}
  • nnzg: [OUT] Vector{Cint}
  • lg: [IN] Vector{Cint}
  • g_val: [OUT] Vector{T}
  • g_var: [OUT] Vector{Cint}
  • grad: [IN] Vector{Bool}
source
CUTEst.ceh — Function
ceh(T, libsif, status, n, m, x, y, ne, lhe_ptr, he_row_ptr, he_val_ptr, lhe_row, he_row, lhe_val, he_val, byrows)

The ceh subroutine evaluates the Hessian matrix of the Lagrangian function l(x,y)=f(x)+yTc(x) for the problem decoded into OUTSDIF.d at the point (x,y)= (X,Y). This Hessian matrix is stored as a sparse matrix in finite element format H=eΣ1He, where each square symmetric element He involves a small subset of the rows of the Hessian matrix. The problem under consideration consists in minimizing (or maximizing) an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • ne: [OUT] Vector{Cint}
  • lhe_ptr: [IN] Vector{Cint}
  • herowptr: [OUT] Vector{Cint}
  • hevalptr: [OUT] Vector{Cint}
  • lhe_row: [IN] Vector{Cint}
  • he_row: [OUT] Vector{Cint}
  • lhe_val: [IN] Vector{Cint}
  • he_val: [OUT] Vector{T}
  • byrows: [IN] Vector{Cint}
source
CUTEst.cdimsh — Function
cdimsh(T, libsif, status, nnzh)

The cdimsh subroutine determines the number of nonzero elements required to store the Hessian matrix of the Lagrangian function for the problem decoded into OUTSDIF.d in the constrained minimization case. The matrix is stored in sparse "coordinate" format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
source
CUTEst.ugr — Function
ugr(T, libsif, status, n, x, g)

The ugr subroutine evaluates the gradient of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • g: [OUT] Vector{T}
source
CUTEst.cshcprod — Function
cshcprod(T, libsif, status, n, m, goth, x, y, nnz_vector, index_nz_vector, vector, nnz_result, index_nz_result, result)

The cshcprod subroutine forms the product of a sparse vector with the Hessian matrix of the constraint part of the Lagrangian function yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • goth: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • nnz_vector: [IN] Vector{Cint}
  • indexnzvector: [IN] Vector{Cint}
  • vector: [IN] Vector{T}
  • nnz_result: [OUT] Vector{Cint}
  • indexnzresult: [OUT] Vector{Cint}
  • result: [OUT] Vector{T}
source
CUTEst.uhprod — Function
uhprod(T, libsif, status, n, goth, x, vector, result)

The uhprod subroutine forms the product of a vector with the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • goth: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • vector: [IN] Vector{T}
  • result: [OUT] Vector{T}
source
CUTEst.csgreh — Function
csgreh(T, libsif, status, n, m, x, y, grlagf, nnzj, lj, j_val, j_var, j_fun, ne, lhe_ptr, he_row_ptr, he_val_ptr, lhe_row, he_row, lhe_val, he_val, byrows)

The csgreh subroutine evaluates both the gradients of the general constraint functions and the Hessian matrix of the Lagrangian function l(x,y)=f(x)+yTc(x) for the problem decoded into OUTSDIF.d at the point (x,y)= (X,Y). This Hessian matrix is stored as a sparse matrix in finite element format H=eΣ1He, where each square symmetric element He involves a small subset of the rows of the Hessian matrix. The subroutine also obtains the gradient of either the objective function or the Lagrangian function, stored in a sparse format. The problem under consideration consists in minimizing (or maximizing) an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • grlagf: [IN] Vector{Bool}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • j_val: [OUT] Vector{T}
  • j_var: [OUT] Vector{Cint}
  • j_fun: [OUT] Vector{Cint}
  • ne: [OUT] Vector{Cint}
  • lhe_ptr: [IN] Vector{Cint}
  • herowptr: [OUT] Vector{Cint}
  • hevalptr: [OUT] Vector{Cint}
  • lhe_row: [IN] Vector{Cint}
  • he_row: [OUT] Vector{Cint}
  • lhe_val: [IN] Vector{Cint}
  • he_val: [OUT] Vector{T}
  • byrows: [IN] Vector{Bool}
source
CUTEst.uterminate — Function
uterminate(T, libsif, status)

The uterminate subroutine deallocates all workspace arrays created since the last call to usetup.

  • status: [OUT] Vector{Cint}
source
CUTEst.ugreh — Function
ugreh(T, libsif, status, n, x, g, ne, lhe_ptr, he_row_ptr, he_val_ptr, lhe_row, he_row, lhe_val, he_val, byrows)

The ugreh subroutine evaluates the gradient and Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a sparse matrix in finite element format H=eΣ1He, where each square symmetric element H sub e involves a small subset of the rows of the Hessian matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • g: [OUT] Vector{T}
  • ne: [OUT] Vector{Cint}
  • lhe_ptr: [IN] Vector{Cint}
  • herowptr: [OUT] Vector{Cint}
  • hevalptr: [OUT] Vector{Cint}
  • lhe_row: [IN] Vector{Cint}
  • he_row: [OUT] Vector{Cint}
  • lhe_val: [IN] Vector{Cint}
  • he_val: [OUT] Vector{T}
  • byrows: [IN] Vector{Bool}
source
CUTEst.cigr — Function
cigr(T, libsif, status, n, iprob, x, g_val)

The cigr subroutine evaluates the gradient of either the objective function or a constraint function of the problem decoded from a SIF file by the script sifdecoder at the point X, in the constrained minimization case.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • iprob: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • g_val: [OUT] Vector{T}
source
CUTEst.clfg — Function
clfg(T, libsif, status, n, m, x, y, f, g, grad)

The clfg subroutine evaluates the value of the Lagrangian function l(x,y)=f(x)+yTc(x) for the problem decoded from a SIF file by the script sifdecoder at the point (X,Y), and possibly its gradient. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • f: [OUT] Vector{T}
  • g: [OUT] Vector{T}
  • grad: [IN] Vector{Bool}
source
CUTEst.ugrdh — Function
ugrdh(T, libsif, status, n, x, g, lh1, h)

The ugrdh subroutine evaluates the gradient and Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a dense matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • g: [OUT] Vector{T}
  • lh1: [IN] Vector{Cint}
  • h: [OUT] Matrix{T}
source
CUTEst.cdhc — Function
cdhc(T, libsif, status, n, m, x, y, lh1, h_val)

The cdhc subroutine evaluates the Hessian matrix of the constraint part of the Lagrangian function yTc(x) for the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The matrix is stored as a dense matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • lh1: [IN] Vector{Cint}
  • h_val: [OUT] Matrix{T}
source
CUTEst.fclose — Function
fclose(T, libsif, funit, status)
source
CUTEst.csetup — Function
csetup(libsif, status, input, out, io_buffer, n, m, x, x_l, x_u, y, c_l, c_u, equatn, linear, e_order, l_order, v_order)

The csetup subroutine sets up the correct data structures for subsequent computations on the problem decoded from a SIF file by the script sifdecoder. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • input: [IN] Vector{Cint}
  • out: [IN] Vector{Cint}
  • io_buffer: [IN] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [OUT] Vector{T}
  • x_l: [OUT] Vector{T}
  • x_u: [OUT] Vector{T}
  • y: [OUT] Vector{T}
  • c_l: [OUT] Vector{T}
  • c_u: [OUT] Vector{T}
  • equatn: [OUT] Vector{Bool}
  • linear: [OUT] Vector{Bool}
  • e_order: [IN] Vector{Cint}
  • l_order: [IN] Vector{Cint}
  • v_order: [IN] Vector{Cint}
source
CUTEst.ccifg — Function
ccifg(T, libsif, status, n, icon, x, ci, gci, grad)

The ccifg subroutine evaluates the value of a particular constraint function of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient in the constrained minimization case. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • icon: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • ci: [OUT] Vector{T}
  • gci: [OUT] Vector{T}
  • grad: [IN] Vector{Bool}
source
CUTEst.cvartype — Function
cvartype(T, libsif, status, n, x_type)

The cvartype subroutine determines the type (continuous, 0-1, integer) of each variable involved in the problem decoded from a SIF file by the script sifdecoder. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x_type: [OUT] Vector{Cint}
source
CUTEst.udimsh — Function
udimsh(T, libsif, status, nnzh)

The udimsh subroutine determine the number of nonzeros required to store the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a sparse matrix in coordinate format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
source
CUTEst.csjprod — Function
csjprod(T, libsif, status, n, m, gotj, jtrans, x, nnz_vector, index_nz_vector, vector, lvector, nnz_result, index_nz_result, result, lresult)

The csjprod subroutine forms the product of a sparse vector with the Jacobian matrix, or with its transpose, of the constraint functions of the problem decoded from a SIF file by the script sifdecoder evaluated at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • gotj: [IN] Vector{Bool}
  • jtrans: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • nnz_vector: [IN] Vector{Cint}
  • indexnzvector: [IN] Vector{Cint}
  • vector: [IN] Vector{T}
  • lvector: [IN] Vector{Cint}
  • nnz_result: [OUT] Vector{Cint}
  • indexnzresult: [OUT] Vector{Cint}
  • result: [OUT] Vector{T}
  • lresult: [IN] Vector{Cint}
source
CUTEst.cgrdh — Function
cgrdh(T, libsif, status, n, m, x, y, grlagf, g, jtrans, lj1, lj2, j_val, lh1, h_val)

The cgrdh subroutine evaluates the gradients of the general constraints and of either the objective function f(x) or the Lagrangian function l(x,y)=f(x)+yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). It also evaluates the Hessian matrix of the Lagrangian function at (x,y). The gradients and matrices are stored in a dense format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • grlagf: [IN] Vector{Bool}
  • g: [OUT] Vector{T}
  • jtrans: [IN] Vector{Bool}
  • lj1: [IN] Vector{Cint}
  • lj2: [IN] Vector{Cint}
  • j_val: [OUT] Matrix{T}
  • lh1: [IN] Vector{Cint}
  • h_val: [OUT] Matrix{T}
source
CUTEst.cdh — Function
cdh(T, libsif, status, n, m, x, y, lh1, h_val)

The cdh subroutine evaluates the Hessian matrix of the Lagrangian function l(x,y)=f(x)+yTc(x) for the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The matrix is stored as a dense matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • lh1: [IN] Vector{Cint}
  • h_val: [OUT] Matrix{T}
source
CUTEst.ush — Function
ush(T, libsif, status, n, x, nnzh, lh, h_val, h_row, h_col)

The ush subroutine evaluates the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a sparse matrix in coordinate format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group- partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_val: [OUT] Vector{T}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.csh — Function
csh(T, libsif, status, n, m, x, y, nnzh, lh, h_val, h_row, h_col)

The csh subroutine evaluates the Hessian of the Lagrangian function l(x,y)=f(x)+yTc(x) for the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The matrix is stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_val: [OUT] Vector{T}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.ueh — Function
ueh(T, libsif, status, n, x, ne, lhe_ptr, he_row_ptr, he_val_ptr, lhe_row, he_row, lhe_val, he_val, byrows)

The ueh subroutine evaluates the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a sparse matrix in finite element format H=eΣ1He, where each square symmetric element He involves a small subset of the rows of the Hessian matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • ne: [OUT] Vector{Cint}
  • lhe_ptr: [IN] Vector{Cint}
  • herowptr: [OUT] Vector{Cint}
  • hevalptr: [OUT] Vector{Cint}
  • lhe_row: [IN] Vector{Cint}
  • he_row: [OUT] Vector{Cint}
  • lhe_val: [IN] Vector{Cint}
  • he_val: [OUT] Vector{T}
  • byrows: [IN] Vector{Bool}
source
CUTEst.cchprods — Function
cchprods(T, libsif, status, n, m, goth, x, vector, lchp, chp_val, chp_ind, chp_ptr)

The cchprods subroutine forms the product of a vector with each of the Hessian matrix of the constraint functions c(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point x= X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • goth: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • vector: [IN] Vector{T}
  • lchp: [IN] Vector{Cint}
  • chp_val: [OUT] Vector{T}
  • chp_ind: [IN] Vector{Cint}
  • chp_ptr: [IN] Vector{Cint}
source
CUTEst.ccifsg — Function
ccifsg(T, libsif, status, n, icon, x, ci, nnzgci, lgci, gci_val, gci_var, grad)

The ccifsg subroutine evaluates the value of a particular constraint function of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient in the constrained minimization case. The gradient is stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • icon: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • ci: [OUT] Vector{T}
  • nnzgci: [OUT] Vector{Cint}
  • lgci: [IN] Vector{Cint}
  • gci_val: [OUT] Vector{T}
  • gci_var: [OUT] Vector{Cint}
  • grad: [IN] Vector{Bool}
source
CUTEst.uofg — Function
uofg(T, libsif, status, n, x, f, g, grad)

The uofg subroutine evaluates the value of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group- partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • f: [OUT] Vector{T}
  • g: [OUT] Vector{T}
  • grad: [IN] Vector{Bool}
source
CUTEst.connames — Function
connames(T, libsif, status, m, cname)

The connames subroutine obtains the names of the general constraints of the problem. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • m: [IN] Vector{Cint}
  • cname: [OUT] Vector{Cchar}

To get useful names, use String(cname[:,i]).

source
CUTEst.cisgr — Function
cisgr(T, libsif, status, n, iprod, x, nnzg, lg, g_val, g_var)

The cisgr subroutine evaluates the gradient of either the objective function or a constraint function of the problem decoded from a SIF file by the script sifdecoder at the point X, in the constrained minimization case. The gradient is stored in sparse format.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • iprob: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • nnzg: [OUT] Vector{Cint}
  • lg: [IN] Vector{Cint}
  • g_val: [OUT] Vector{T}
  • g_var: [OUT] Vector{Cint}
source
CUTEst.udh — Function
udh(T, libsif, status, n, x, lh1, h)

The udh subroutine evaluates the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a dense matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • lh1: [IN] Vector{Cint}
  • h: [OUT] Matrix{T}
source
CUTEst.cdimse — Function
cdimse(T, libsif, status, ne, he_val_ne, he_row_ne)

The cdimse subroutine determines the number of nonzero elements required to store the Hessian matrix of the Lagrangian function for the problem decoded from a SIF file by the script sifdecoder. The matrix is stored in sparse "finite element" format H=eΣ1He, where each square symmetric element He involves a small subset of the rows of the Hessian matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • ne: [OUT] Vector{Cint}
  • hevalne: [OUT] Vector{Cint}
  • herowne: [OUT] Vector{Cint}
source
CUTEst.ushp — Function
ushp(T, libsif, status, n, nnzh, lh, h_row, h_col)

The ushp subroutine evaluates the sparsity pattern of the Hessian matrix of the objective function of the problem, decoded from a SIF file by the script sifdecoder, in coordinate format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.creport — Function
creport(T, libsif, status, calls, time)

The creport subroutine obtains statistics concerning function evaluation and CPU time used for constrained optimization in a standardized format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • calls: [OUT] Vector{T}
  • time: [OUT] Vector{T}
source
CUTEst.cshp — Function
cshp(T, libsif, status, n, nnzh, lh, h_row, h_col)

The cshp subroutine evaluates the sparsity pattern of the Hessian of the Lagrangian function l(x,y)=f(x)+yTc(x) for the problem, decoded from a SIF file by the script sifdecoder, in coordinate format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.probname — Function
probname(T, libsif, status, pname)

The probname subroutine obtains the name of the problem. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • pname: [OUT] Vector{Cchar}

To get a useful name, use String(pname).

source
CUTEst.unames — Function
unames(T, libsif, status, n, pname, vname)

The unames subroutine obtains the names of the problem and its variables. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • pname: [OUT] Vector{Cchar}
  • vname: [OUT] Vector{Cchar}

To get useful names, use String(x) where x can be pname or vname[:,i].

source
CUTEst.cidh — Function
cidh(T, libsif, status, n, x, iprob, lh1, h)

The cidh subroutine evaluates the Hessian matrix of either the objective function or a constraint function for the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient. The matrix is stored as a dense matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • iprob: [IN] Vector{Cint}
  • lh1: [IN] Vector{Cint}
  • h: [OUT] Matrix{T}
source
CUTEst.csgr — Function
csgr(T, libsif, status, n, m, x, y, grlagf, nnzj, lj, j_val, j_var, j_fun)

The csgr subroutine evaluates the gradients of the general constraints and of either the objective function or the Lagrangian function l(x,y)=f(x)+yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). It also evaluates the Hessian matrix of the Lagrangian function at (x,y). The gradients are stored in a sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • grlagf: [IN] Vector{Bool}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • j_val: [OUT] Vector{T}
  • j_var: [OUT] Vector{Cint}
  • j_fun: [OUT] Vector{Cint}
source
CUTEst.usetup — Function
usetup(T, libsif, status, input, out, io_buffer, n, x, x_l, x_u)

The usetup subroutine sets up the correct data structures for subsequent computations in the case where the only possible constraints are bound constraints. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group- partially separable.

  • status: [OUT] Vector{Cint}
  • input: [IN] Vector{Cint}
  • out: [IN] Vector{Cint}
  • io_buffer: [IN] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [OUT] Vector{T}
  • x_l: [OUT] Vector{T}
  • x_u: [OUT] Vector{T}
source
CUTEst.csgrsh — Function
csgrsh(T, libsif, status, n, m, x, y, grlagf, nnzj, lj, j_val, j_var, j_fun, nnzh, lh, h_val, h_row, h_col)

The csgrsh subroutine evaluates the gradients of the general constraints, the Hessian matrix of the Lagrangian function l(x,y)=f(x)+yTc(x) and the gradient of either the objective function or the Lagrangian corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The data is stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • grlagf: [IN] Vector{Bool}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • j_val: [OUT] Vector{T}
  • j_var: [OUT] Vector{Cint}
  • j_fun: [OUT] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_val: [OUT] Vector{T}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.cdimen — Function
cdimen(T, libsif, status, input, n, m)

The cdimen subroutine discovers how many variables and constraints are involved in the problem decoded from a SIF file by the script sifdecoder. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • input: [IN] Vector{Cint}
  • n: [OUT] Vector{Cint}
  • m: [OUT] Vector{Cint}
source
CUTEst.cshprod — Function
cshprod(T, libsif, status, n, m, goth, x, y, nnz_vector, index_nz_vector, vector, nnz_result, index_nz_result, result)

The cshprod subroutine forms the product of a sparse vector with the Hessian matrix of the Lagrangian function l(x,y)=f(x)+yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • goth: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • nnz_vector: [IN] Vector{Cint}
  • indexnzvector: [IN] Vector{Cint}
  • vector: [IN] Vector{T}
  • nnz_result: [OUT] Vector{Cint}
  • indexnzresult: [OUT] Vector{Cint}
  • result: [OUT] Vector{T}

Notice that vector and result should have allocated dimension of n.

source
CUTEst.cchprodsp — Function
cchprodsp(T, libsif, status, m, lchp, chp_ind, chp_ptr)

The cchprodsp subroutine obtains the sparsity structure used when forming the product of a vector with each of the Hessian matrices of the constraint functions c(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point x= X.

  • status: [OUT] Vector{Cint}
  • m: [IN] Vector{Cint}
  • lchp: [IN] Vector{Cint}
  • chp_ind: [IN] Vector{Cint}
  • chp_ptr: [IN] Vector{Cint}
source
CUTEst.cconst — Function
cconst(T, libsif, status, m, c)
source
CUTEst.udimen — Function
udimen(T, libsif, status, input, n)

The udimen subroutine discovers how many variables are involved in the problem decoded from a SIF file by the script sifdecoder. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • input: [IN] Vector{Cint}
  • n: [OUT] Vector{Cint}
source
CUTEst.pname — Function
pname(T, libsif, status, input, pname)

The pname subroutine obtains the name of the problem directly from the datafile OUTSDIF.d that was created by the script sifdecoder when decoding a SIF file. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • input: [IN] Vector{Cint}
  • pname: [OUT] Vector{Cchar}
source
CUTEst.chprod — Function
chprod(T, libsif, status, n, m, goth, x, y, vector, result)

The chprod subroutine forms the product of a vector with the Hessian matrix of the Lagrangian function l(x,y)=f(x)+yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • goth: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • vector: [IN] Vector{T}
  • result: [OUT] Vector{T}
source
CUTEst.cterminate — Function
cterminate(T, libsif, status)

The uterminate subroutine deallocates all workspace arrays created since the last call to csetup.

  • status: [OUT] Vector{Cint}
source
CUTEst.cifn — Function
cifn(T, libsif, status, n, iprob, x, f)

The cifn subroutine evaluates the value of either the objective function or a constrainted function of the problem decoded from a SIF file by the script sifdecoder at the point X, in the constrained minimization case.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • iprob: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • f: [OUT] Vector{T}
source
CUTEst.cstats — Function
cstats(T, libsif, status, nonlinear_variables_objective, nonlinear_variables_constraints, equality_constraints, linear_constraints)
  • status: [OUT] Vector{Cint}
  • nonlinearvariablesobjective: [OUT] Vector{Cint}
  • nonlinearvariablesconstraints: [OUT] Vector{Cint}
  • equality_constraints: [OUT] Vector{Cint}
  • linear_constraints: [OUT] Vector{Cint}
source
CUTEst.cnames — Function
cnames(T, libsif, status, n, m, pname, vname, cname)

The cnames subroutine obtains the names of the problem, its variables and general constraints. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • pname: [OUT] Vector{Cchar}
  • vname: [OUT] Vector{Cchar}
  • cname: [OUT] Vector{Cchar}

To get useful names, use String(x) where x can be pname, vname[:,i], or cname[:,i].

source
CUTEst.ugrsh — Function
ugrsh(T, libsif, status, n, x, g, nnzh, lh, h_val, h_row, h_col)

The ugrsh subroutine evaluates the gradient and Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a sparse matrix in coordinate format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group- partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • g: [OUT] Vector{T}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_val: [OUT] Vector{T}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.varnames — Function
varnames(T, libsif, status, n, vname)

The varnames subroutine obtains the names of the problem variables. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • vname: [OUT] Vector{Cchar}

To get useful names, use String(vname[:, i]).

source
CUTEst.uvartype — Function
uvartype(T, libsif, status, n, x_type)

The uvartype subroutine determines the type (continuous, 0-1, integer) of each variable involved in the problem decoded from a SIF file by the script sifdecoder. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x_type: [OUT] Vector{Cint}
source
CUTEst.fopen — Function
fopen(T, libsif, funit, outsdif, status)
source
CUTEst.csgrshp — Function
csgrshp(T, libsif, status, n, nnzj, lj, j_var, j_fun, nnzh, lh, h_row, h_col)

The csgrshp subroutine evaluates sparsity pattern used when storing the gradients of the general constraints and of either the objective function or the Lagrangian function l(x,y)=f(x)+yTc(x), as well as the Hessian of the Lagrangian function, corresponding to the problem decoded from a SIF file by the script sifdecoder.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • j_var: [OUT] Vector{Cint}
  • j_fun: [OUT] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.ccfsg — Function
ccfsg(T, libsif, status, n, m, x, c, nnzj, lj, j_val, j_var, j_fun, grad)

The ccfsg subroutine evaluates the values of the constraint functions of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly their gradients in the constrained minimization case. The gradients are stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • c: [OUT] Vector{T}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • j_val: [OUT] Vector{T}
  • j_var: [OUT] Vector{Cint}
  • j_fun: [OUT] Vector{Cint}
  • grad: [IN] Vector{Bool}
source
CUTEst.classification — Function
classification(T, libsif, status, funit, class)
source
+println(f[])
CUTEst.cjprod — Function
cjprod(T, libsif, status, n, m, gotj, jtrans, x, vector, lvector, result, lresult)

The cjprod subroutine forms the product of a vector with the Jacobian matrix, or with its transpose, of the constraint functions of the problem decoded from a SIF file by the script sifdecoder evaluated at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • gotj: [IN] Vector{Bool}
  • jtrans: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • vector: [IN] Vector{T}
  • lvector: [IN] Vector{Cint}
  • result: [OUT] Vector{T}
  • lresult: [IN] Vector{Cint}
source
CUTEst.ccfg — Function
ccfg(T, libsif, status, n, m, x, c, jtrans, lcjac1, lcjac2, cjac, grad)

The ccfg subroutine evaluates the values of the constraint functions of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly their gradients. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • c: [OUT] Vector{T}
  • jtrans: [IN] Vector{Bool}
  • lcjac1: [IN] Vector{Cint}
  • lcjac2: [IN] Vector{Cint}
  • cjac: [OUT] Matrix{T}
  • grad: [IN] Vector{Bool}
source
CUTEst.ccf — Function
ccf(T, libsif, status, n, m, x, c)
source
CUTEst.cish — Function
cish(T, libsif, status, n, x, iprob, nnzh, lh, h_val, h_row, h_col)

The cish subroutine evaluates the Hessian of a particular constraint function or the objective function for the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient. The matrix is stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • iprob: [IN] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_val: [OUT] Vector{T}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.ushprod — Function
ushprod(T, libsif, status, n, goth, x, nnz_vector, index_nz_vector, vector, nnz_result, index_nz_result, result)

The ushprod subroutine forms the product of a sparse vector with the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • goth: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • nnz_vector: [IN] Vector{Cint}
  • indexnzvector: [IN] Vector{Cint}
  • vector: [IN] Vector{T}
  • nnz_result: [OUT] Vector{Cint}
  • indexnzresult: [OUT] Vector{Cint}
  • result: [OUT] Vector{T}

Notice that vector and result should have allocated dimension of n.

source
CUTEst.cdimchp — Function
cdimchp(T, libsif, status, nnzchp)

The cdimchp subroutine determines the number of nonzero elements required to store the products of the Hessian matrices of the constraint functions with a specified vector for the problem decoded into OUTSDIF.d in the constrained minimization case. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • nnzchp: [OUT] Vector{Cint}
source
CUTEst.ureport — Function
ureport(T, libsif, status, calls, time)

The ureport subroutine obtains statistics concerning function evaluation and CPU time used for unconstrained or bound-constrained optimization in a standardized format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group- partially separable.

  • status: [OUT] Vector{Cint}
  • calls: [OUT] Vector{T}
  • time: [OUT] Vector{T}
source
CUTEst.chcprod — Function
chcprod(T, libsif, status, n, m, goth, x, y, vector, result)

The chcprod subroutine forms the product of a vector with the Hessian matrix of the constraint part of the Lagrangian function yTc(x) of the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • goth: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • vector: [IN] Vector{T}
  • result: [OUT] Vector{T}
source
CUTEst.udimse — Function
udimse(T, libsif, status, ne, he_val_ne, he_row_ne)

The udimse subroutine determine the number of nonzeros required to store the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a sparse matrix in finite element format H=eΣ1He, where each square symmetric element H_i involves a small subset of the rows of the Hessian matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • ne: [OUT] Vector{Cint}
  • hevalne: [OUT] Vector{Cint}
  • herowne: [OUT] Vector{Cint}
source
CUTEst.ubandh — Function
ubandh(T, libsif, status, n, x, semibandwidth, h_band, lbandh, max_semibandwidth)

The ubandh subroutine extracts the elements which lie within a band of given semi-bandwidth out of the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group- partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • semibandwidth: [IN] Vector{Cint}
  • h_band: [OUT] Matrix{T}
  • lbandh: [IN] Vector{Cint}
  • max_semibandwidth: [OUT] Vector{Cint}
source
CUTEst.csjp — Function
csjp(T, libsif, status, nnzj, lj, jvar, jcon)

The csjp subroutine evaluates the sparsity pattern of the Jacobian of the constraints for a problem decoded from a SIF file by the script sifdecoder.

  • status: [OUT] Vector{Cint}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • jvar: [OUT] Vector{Cint}
  • jcon: [OUT] Vector{Cint}
source
CUTEst.cgr — Function
cgr(T, libsif, status, n, m, x, y, grlagf, g, jtrans, lj1, lj2, j_val)

The cgr subroutine evaluates the gradients of the general constraints and of either the objective function f(x) or the Lagrangian function l(x,y)=f(x)+yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • grlagf: [IN] Vector{Bool}
  • g: [OUT] Vector{T}
  • jtrans: [IN] Vector{Bool}
  • lj1: [IN] Vector{Cint}
  • lj2: [IN] Vector{Cint}
  • j_val: [OUT] Matrix{T}
source
CUTEst.ufn — Function
ufn(T, libsif, status, n, x, f)

The ufn subroutine evaluates the value of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • f: [OUT] Vector{T}
source
CUTEst.csgrp — Function
csgrp(T, libsif, status, n, nnzj, lj, j_var, j_fun)

The csgrp subroutine evaluates sparsity pattern used when storing the gradients of the general constraints and of either the objective function or the Lagrangian function l(x,y)=f(x)+yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • j_var: [OUT] Vector{Cint}
  • j_fun: [OUT] Vector{Cint}
source
CUTEst.cshc — Function
cshc(T, libsif, status, n, m, x, y, nnzh, lh, h_val, h_row, h_col)

The cshc subroutine evaluates the Hessian matrix of the constraint part of the Lagrangian function yTc(x) for the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The matrix is stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_val: [OUT] Vector{T}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.cdimsj — Function
cdimsj(T, libsif, status, nnzj)

The cdimsj subroutine determines the number of nonzero elements required to store the matrix of gradients of the objective function and constraint functions for the problem decoded into OUTSDIF.d in the constrained minimization case. The matrix is stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • nnzj: [OUT] Vector{Cint}
source
CUTEst.cfn — Function
cfn(T, libsif, status, n, m, x, f, c)

The cfn subroutine evaluates the value of the objective function and general constraint functions of the problem decoded from a SIF file by the script sifdecoder at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • f: [OUT] Vector{T}
  • c: [OUT] Vector{T}
source
CUTEst.cofg — Function
cofg(T, libsif, status, n, x, f, g, grad)

The cofg subroutine evaluates the value of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • f: [OUT] Vector{T}
  • g: [OUT] Vector{T}
  • grad: [IN] Vector{Bool}
source
CUTEst.cofsg — Function
cofsg(T, libsif, status, n, x, f, nnzg, lg, g_val, g_var, grad)

The cofsg subroutine evaluates the value of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • f: [OUT] Vector{T}
  • nnzg: [OUT] Vector{Cint}
  • lg: [IN] Vector{Cint}
  • g_val: [OUT] Vector{T}
  • g_var: [OUT] Vector{Cint}
  • grad: [IN] Vector{Bool}
source
CUTEst.ceh — Function
ceh(T, libsif, status, n, m, x, y, ne, lhe_ptr, he_row_ptr, he_val_ptr, lhe_row, he_row, lhe_val, he_val, byrows)

The ceh subroutine evaluates the Hessian matrix of the Lagrangian function l(x,y)=f(x)+yTc(x) for the problem decoded into OUTSDIF.d at the point (x,y)= (X,Y). This Hessian matrix is stored as a sparse matrix in finite element format H=eΣ1He, where each square symmetric element He involves a small subset of the rows of the Hessian matrix. The problem under consideration consists in minimizing (or maximizing) an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • ne: [OUT] Vector{Cint}
  • lhe_ptr: [IN] Vector{Cint}
  • herowptr: [OUT] Vector{Cint}
  • hevalptr: [OUT] Vector{Cint}
  • lhe_row: [IN] Vector{Cint}
  • he_row: [OUT] Vector{Cint}
  • lhe_val: [IN] Vector{Cint}
  • he_val: [OUT] Vector{T}
  • byrows: [IN] Vector{Cint}
source
CUTEst.cdimsh — Function
cdimsh(T, libsif, status, nnzh)

The cdimsh subroutine determines the number of nonzero elements required to store the Hessian matrix of the Lagrangian function for the problem decoded into OUTSDIF.d in the constrained minimization case. The matrix is stored in sparse "coordinate" format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
source
CUTEst.ugr — Function
ugr(T, libsif, status, n, x, g)

The ugr subroutine evaluates the gradient of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • g: [OUT] Vector{T}
source
CUTEst.cshcprod — Function
cshcprod(T, libsif, status, n, m, goth, x, y, nnz_vector, index_nz_vector, vector, nnz_result, index_nz_result, result)

The cshcprod subroutine forms the product of a sparse vector with the Hessian matrix of the constraint part of the Lagrangian function yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • goth: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • nnz_vector: [IN] Vector{Cint}
  • indexnzvector: [IN] Vector{Cint}
  • vector: [IN] Vector{T}
  • nnz_result: [OUT] Vector{Cint}
  • indexnzresult: [OUT] Vector{Cint}
  • result: [OUT] Vector{T}
source
CUTEst.uhprod — Function
uhprod(T, libsif, status, n, goth, x, vector, result)

The uhprod subroutine forms the product of a vector with the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • goth: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • vector: [IN] Vector{T}
  • result: [OUT] Vector{T}
source
CUTEst.csgreh — Function
csgreh(T, libsif, status, n, m, x, y, grlagf, nnzj, lj, j_val, j_var, j_fun, ne, lhe_ptr, he_row_ptr, he_val_ptr, lhe_row, he_row, lhe_val, he_val, byrows)

The csgreh subroutine evaluates both the gradients of the general constraint functions and the Hessian matrix of the Lagrangian function l(x,y)=f(x)+yTc(x) for the problem decoded into OUTSDIF.d at the point (x,y)= (X,Y). This Hessian matrix is stored as a sparse matrix in finite element format H=eΣ1He, where each square symmetric element He involves a small subset of the rows of the Hessian matrix. The subroutine also obtains the gradient of either the objective function or the Lagrangian function, stored in a sparse format. The problem under consideration consists in minimizing (or maximizing) an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • grlagf: [IN] Vector{Bool}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • j_val: [OUT] Vector{T}
  • j_var: [OUT] Vector{Cint}
  • j_fun: [OUT] Vector{Cint}
  • ne: [OUT] Vector{Cint}
  • lhe_ptr: [IN] Vector{Cint}
  • herowptr: [OUT] Vector{Cint}
  • hevalptr: [OUT] Vector{Cint}
  • lhe_row: [IN] Vector{Cint}
  • he_row: [OUT] Vector{Cint}
  • lhe_val: [IN] Vector{Cint}
  • he_val: [OUT] Vector{T}
  • byrows: [IN] Vector{Bool}
source
CUTEst.uterminate — Function
uterminate(T, libsif, status)

The uterminate subroutine deallocates all workspace arrays created since the last call to usetup.

  • status: [OUT] Vector{Cint}
source
CUTEst.ugreh — Function
ugreh(T, libsif, status, n, x, g, ne, lhe_ptr, he_row_ptr, he_val_ptr, lhe_row, he_row, lhe_val, he_val, byrows)

The ugreh subroutine evaluates the gradient and Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a sparse matrix in finite element format H=eΣ1He, where each square symmetric element H sub e involves a small subset of the rows of the Hessian matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • g: [OUT] Vector{T}
  • ne: [OUT] Vector{Cint}
  • lhe_ptr: [IN] Vector{Cint}
  • herowptr: [OUT] Vector{Cint}
  • hevalptr: [OUT] Vector{Cint}
  • lhe_row: [IN] Vector{Cint}
  • he_row: [OUT] Vector{Cint}
  • lhe_val: [IN] Vector{Cint}
  • he_val: [OUT] Vector{T}
  • byrows: [IN] Vector{Bool}
source
CUTEst.cigr — Function
cigr(T, libsif, status, n, iprob, x, g_val)

The cigr subroutine evaluates the gradient of either the objective function or a constraint function of the problem decoded from a SIF file by the script sifdecoder at the point X, in the constrained minimization case.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • iprob: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • g_val: [OUT] Vector{T}
source
CUTEst.clfg — Function
clfg(T, libsif, status, n, m, x, y, f, g, grad)

The clfg subroutine evaluates the value of the Lagrangian function l(x,y)=f(x)+yTc(x) for the problem decoded from a SIF file by the script sifdecoder at the point (X,Y), and possibly its gradient. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • f: [OUT] Vector{T}
  • g: [OUT] Vector{T}
  • grad: [IN] Vector{Bool}
source
CUTEst.ugrdh — Function
ugrdh(T, libsif, status, n, x, g, lh1, h)

The ugrdh subroutine evaluates the gradient and Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a dense matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • g: [OUT] Vector{T}
  • lh1: [IN] Vector{Cint}
  • h: [OUT] Matrix{T}
source
CUTEst.cdhc — Function
cdhc(T, libsif, status, n, m, x, y, lh1, h_val)

The cdhc subroutine evaluates the Hessian matrix of the constraint part of the Lagrangian function yTc(x) for the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The matrix is stored as a dense matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • lh1: [IN] Vector{Cint}
  • h_val: [OUT] Matrix{T}
source
CUTEst.fclose — Function
fclose(T, libsif, funit, status)
source
CUTEst.csetup — Function
csetup(libsif, status, input, out, io_buffer, n, m, x, x_l, x_u, y, c_l, c_u, equatn, linear, e_order, l_order, v_order)

The csetup subroutine sets up the correct data structures for subsequent computations on the problem decoded from a SIF file by the script sifdecoder. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • input: [IN] Vector{Cint}
  • out: [IN] Vector{Cint}
  • io_buffer: [IN] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [OUT] Vector{T}
  • x_l: [OUT] Vector{T}
  • x_u: [OUT] Vector{T}
  • y: [OUT] Vector{T}
  • c_l: [OUT] Vector{T}
  • c_u: [OUT] Vector{T}
  • equatn: [OUT] Vector{Bool}
  • linear: [OUT] Vector{Bool}
  • e_order: [IN] Vector{Cint}
  • l_order: [IN] Vector{Cint}
  • v_order: [IN] Vector{Cint}
source
CUTEst.ccifg — Function
ccifg(T, libsif, status, n, icon, x, ci, gci, grad)

The ccifg subroutine evaluates the value of a particular constraint function of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient in the constrained minimization case. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • icon: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • ci: [OUT] Vector{T}
  • gci: [OUT] Vector{T}
  • grad: [IN] Vector{Bool}
source
CUTEst.cvartype — Function
cvartype(T, libsif, status, n, x_type)

The cvartype subroutine determines the type (continuous, 0-1, integer) of each variable involved in the problem decoded from a SIF file by the script sifdecoder. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x_type: [OUT] Vector{Cint}
source
CUTEst.udimsh — Function
udimsh(T, libsif, status, nnzh)

The udimsh subroutine determine the number of nonzeros required to store the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a sparse matrix in coordinate format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
source
CUTEst.csjprod — Function
csjprod(T, libsif, status, n, m, gotj, jtrans, x, nnz_vector, index_nz_vector, vector, lvector, nnz_result, index_nz_result, result, lresult)

The csjprod subroutine forms the product of a sparse vector with the Jacobian matrix, or with its transpose, of the constraint functions of the problem decoded from a SIF file by the script sifdecoder evaluated at the point X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • gotj: [IN] Vector{Bool}
  • jtrans: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • nnz_vector: [IN] Vector{Cint}
  • indexnzvector: [IN] Vector{Cint}
  • vector: [IN] Vector{T}
  • lvector: [IN] Vector{Cint}
  • nnz_result: [OUT] Vector{Cint}
  • indexnzresult: [OUT] Vector{Cint}
  • result: [OUT] Vector{T}
  • lresult: [IN] Vector{Cint}
source
CUTEst.cgrdh — Function
cgrdh(T, libsif, status, n, m, x, y, grlagf, g, jtrans, lj1, lj2, j_val, lh1, h_val)

The cgrdh subroutine evaluates the gradients of the general constraints and of either the objective function f(x) or the Lagrangian function l(x,y)=f(x)+yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). It also evaluates the Hessian matrix of the Lagrangian function at (x,y). The gradients and matrices are stored in a dense format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • grlagf: [IN] Vector{Bool}
  • g: [OUT] Vector{T}
  • jtrans: [IN] Vector{Bool}
  • lj1: [IN] Vector{Cint}
  • lj2: [IN] Vector{Cint}
  • j_val: [OUT] Matrix{T}
  • lh1: [IN] Vector{Cint}
  • h_val: [OUT] Matrix{T}
source
CUTEst.cdh — Function
cdh(T, libsif, status, n, m, x, y, lh1, h_val)

The cdh subroutine evaluates the Hessian matrix of the Lagrangian function l(x,y)=f(x)+yTc(x) for the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The matrix is stored as a dense matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • lh1: [IN] Vector{Cint}
  • h_val: [OUT] Matrix{T}
source
CUTEst.ush — Function
ush(T, libsif, status, n, x, nnzh, lh, h_val, h_row, h_col)

The ush subroutine evaluates the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a sparse matrix in coordinate format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group- partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_val: [OUT] Vector{T}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.csh — Function
csh(T, libsif, status, n, m, x, y, nnzh, lh, h_val, h_row, h_col)

The csh subroutine evaluates the Hessian of the Lagrangian function l(x,y)=f(x)+yTc(x) for the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The matrix is stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_val: [OUT] Vector{T}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.ueh — Function
ueh(T, libsif, status, n, x, ne, lhe_ptr, he_row_ptr, he_val_ptr, lhe_row, he_row, lhe_val, he_val, byrows)

The ueh subroutine evaluates the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a sparse matrix in finite element format H=eΣ1He, where each square symmetric element He involves a small subset of the rows of the Hessian matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • ne: [OUT] Vector{Cint}
  • lhe_ptr: [IN] Vector{Cint}
  • herowptr: [OUT] Vector{Cint}
  • hevalptr: [OUT] Vector{Cint}
  • lhe_row: [IN] Vector{Cint}
  • he_row: [OUT] Vector{Cint}
  • lhe_val: [IN] Vector{Cint}
  • he_val: [OUT] Vector{T}
  • byrows: [IN] Vector{Bool}
source
CUTEst.cchprods — Function
cchprods(T, libsif, status, n, m, goth, x, vector, lchp, chp_val, chp_ind, chp_ptr)

The cchprods subroutine forms the product of a vector with each of the Hessian matrix of the constraint functions c(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point x= X. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • goth: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • vector: [IN] Vector{T}
  • lchp: [IN] Vector{Cint}
  • chp_val: [OUT] Vector{T}
  • chp_ind: [IN] Vector{Cint}
  • chp_ptr: [IN] Vector{Cint}
source
CUTEst.ccifsg — Function
ccifsg(T, libsif, status, n, icon, x, ci, nnzgci, lgci, gci_val, gci_var, grad)

The ccifsg subroutine evaluates the value of a particular constraint function of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient in the constrained minimization case. The gradient is stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • icon: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • ci: [OUT] Vector{T}
  • nnzgci: [OUT] Vector{Cint}
  • lgci: [IN] Vector{Cint}
  • gci_val: [OUT] Vector{T}
  • gci_var: [OUT] Vector{Cint}
  • grad: [IN] Vector{Bool}
source
CUTEst.uofg — Function
uofg(T, libsif, status, n, x, f, g, grad)

The uofg subroutine evaluates the value of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group- partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • f: [OUT] Vector{T}
  • g: [OUT] Vector{T}
  • grad: [IN] Vector{Bool}
source
CUTEst.connames — Function
connames(T, libsif, status, m, cname)

The connames subroutine obtains the names of the general constraints of the problem. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • m: [IN] Vector{Cint}
  • cname: [OUT] Vector{Cchar}

To get useful names, use String(cname[:,i]).

source
CUTEst.cisgr — Function
cisgr(T, libsif, status, n, iprod, x, nnzg, lg, g_val, g_var)

The cisgr subroutine evaluates the gradient of either the objective function or a constraint function of the problem decoded from a SIF file by the script sifdecoder at the point X, in the constrained minimization case. The gradient is stored in sparse format.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • iprob: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • nnzg: [OUT] Vector{Cint}
  • lg: [IN] Vector{Cint}
  • g_val: [OUT] Vector{T}
  • g_var: [OUT] Vector{Cint}
source
CUTEst.udh — Function
udh(T, libsif, status, n, x, lh1, h)

The udh subroutine evaluates the Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a dense matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • lh1: [IN] Vector{Cint}
  • h: [OUT] Matrix{T}
source
CUTEst.cdimse — Function
cdimse(T, libsif, status, ne, he_val_ne, he_row_ne)

The cdimse subroutine determines the number of nonzero elements required to store the Hessian matrix of the Lagrangian function for the problem decoded from a SIF file by the script sifdecoder. The matrix is stored in sparse "finite element" format H=eΣ1He, where each square symmetric element He involves a small subset of the rows of the Hessian matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • ne: [OUT] Vector{Cint}
  • hevalne: [OUT] Vector{Cint}
  • herowne: [OUT] Vector{Cint}
source
CUTEst.ushp — Function
ushp(T, libsif, status, n, nnzh, lh, h_row, h_col)

The ushp subroutine evaluates the sparsity pattern of the Hessian matrix of the objective function of the problem, decoded from a SIF file by the script sifdecoder, in coordinate format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.creport — Function
creport(T, libsif, status, calls, time)

The creport subroutine obtains statistics concerning function evaluation and CPU time used for constrained optimization in a standardized format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • calls: [OUT] Vector{T}
  • time: [OUT] Vector{T}
source
CUTEst.cshp — Function
cshp(T, libsif, status, n, nnzh, lh, h_row, h_col)

The cshp subroutine evaluates the sparsity pattern of the Hessian of the Lagrangian function l(x,y)=f(x)+yTc(x) for the problem, decoded from a SIF file by the script sifdecoder, in coordinate format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.probname — Function
probname(T, libsif, status, pname)

The probname subroutine obtains the name of the problem. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • pname: [OUT] Vector{Cchar}

To get a useful name, use String(pname).

source
CUTEst.unames — Function
unames(T, libsif, status, n, pname, vname)

The unames subroutine obtains the names of the problem and its variables. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • pname: [OUT] Vector{Cchar}
  • vname: [OUT] Vector{Cchar}

To get useful names, use String(x) where x can be pname or vname[:,i].

source
CUTEst.cidh — Function
cidh(T, libsif, status, n, x, iprob, lh1, h)

The cidh subroutine evaluates the Hessian matrix of either the objective function or a constraint function for the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly its gradient. The matrix is stored as a dense matrix. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • iprob: [IN] Vector{Cint}
  • lh1: [IN] Vector{Cint}
  • h: [OUT] Matrix{T}
source
CUTEst.csgr — Function
csgr(T, libsif, status, n, m, x, y, grlagf, nnzj, lj, j_val, j_var, j_fun)

The csgr subroutine evaluates the gradients of the general constraints and of either the objective function or the Lagrangian function l(x,y)=f(x)+yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). It also evaluates the Hessian matrix of the Lagrangian function at (x,y). The gradients are stored in a sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • grlagf: [IN] Vector{Bool}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • j_val: [OUT] Vector{T}
  • j_var: [OUT] Vector{Cint}
  • j_fun: [OUT] Vector{Cint}
source
CUTEst.usetup — Function
usetup(T, libsif, status, input, out, io_buffer, n, x, x_l, x_u)

The usetup subroutine sets up the correct data structures for subsequent computations in the case where the only possible constraints are bound constraints. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group- partially separable.

  • status: [OUT] Vector{Cint}
  • input: [IN] Vector{Cint}
  • out: [IN] Vector{Cint}
  • io_buffer: [IN] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [OUT] Vector{T}
  • x_l: [OUT] Vector{T}
  • x_u: [OUT] Vector{T}
source
CUTEst.csgrsh — Function
csgrsh(T, libsif, status, n, m, x, y, grlagf, nnzj, lj, j_val, j_var, j_fun, nnzh, lh, h_val, h_row, h_col)

The csgrsh subroutine evaluates the gradients of the general constraints, the Hessian matrix of the Lagrangian function l(x,y)=f(x)+yTc(x) and the gradient of either the objective function or the Lagrangian corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The data is stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • grlagf: [IN] Vector{Bool}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • j_val: [OUT] Vector{T}
  • j_var: [OUT] Vector{Cint}
  • j_fun: [OUT] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_val: [OUT] Vector{T}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.cdimen — Function
cdimen(T, libsif, status, input, n, m)

The cdimen subroutine discovers how many variables and constraints are involved in the problem decoded from a SIF file by the script sifdecoder. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • input: [IN] Vector{Cint}
  • n: [OUT] Vector{Cint}
  • m: [OUT] Vector{Cint}
source
CUTEst.cshprod — Function
cshprod(T, libsif, status, n, m, goth, x, y, nnz_vector, index_nz_vector, vector, nnz_result, index_nz_result, result)

The cshprod subroutine forms the product of a sparse vector with the Hessian matrix of the Lagrangian function l(x,y)=f(x)+yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • goth: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • nnz_vector: [IN] Vector{Cint}
  • indexnzvector: [IN] Vector{Cint}
  • vector: [IN] Vector{T}
  • nnz_result: [OUT] Vector{Cint}
  • indexnzresult: [OUT] Vector{Cint}
  • result: [OUT] Vector{T}

Notice that vector and result should have allocated dimension of n.

source
CUTEst.cchprodsp — Function
cchprodsp(T, libsif, status, m, lchp, chp_ind, chp_ptr)

The cchprodsp subroutine obtains the sparsity structure used when forming the product of a vector with each of the Hessian matrices of the constraint functions c(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point x= X.

  • status: [OUT] Vector{Cint}
  • m: [IN] Vector{Cint}
  • lchp: [IN] Vector{Cint}
  • chp_ind: [IN] Vector{Cint}
  • chp_ptr: [IN] Vector{Cint}
source
CUTEst.cconst — Function
cconst(T, libsif, status, m, c)
source
CUTEst.udimen — Function
udimen(T, libsif, status, input, n)

The udimen subroutine discovers how many variables are involved in the problem decoded from a SIF file by the script sifdecoder. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • input: [IN] Vector{Cint}
  • n: [OUT] Vector{Cint}
source
CUTEst.pname — Function
pname(T, libsif, status, input, pname)

The pname subroutine obtains the name of the problem directly from the datafile OUTSDIF.d that was created by the script sifdecoder when decoding a SIF file. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • input: [IN] Vector{Cint}
  • pname: [OUT] Vector{Cchar}
source
CUTEst.chprod — Function
chprod(T, libsif, status, n, m, goth, x, y, vector, result)

The chprod subroutine forms the product of a vector with the Hessian matrix of the Lagrangian function l(x,y)=f(x)+yTc(x) corresponding to the problem decoded from a SIF file by the script sifdecoder at the point (x,y)= (X,Y). The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • goth: [IN] Vector{Bool}
  • x: [IN] Vector{T}
  • y: [IN] Vector{T}
  • vector: [IN] Vector{T}
  • result: [OUT] Vector{T}
source
CUTEst.cterminate — Function
cterminate(T, libsif, status)

The uterminate subroutine deallocates all workspace arrays created since the last call to csetup.

  • status: [OUT] Vector{Cint}
source
CUTEst.cifn — Function
cifn(T, libsif, status, n, iprob, x, f)

The cifn subroutine evaluates the value of either the objective function or a constrainted function of the problem decoded from a SIF file by the script sifdecoder at the point X, in the constrained minimization case.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • iprob: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • f: [OUT] Vector{T}
source
CUTEst.cstats — Function
cstats(T, libsif, status, nonlinear_variables_objective, nonlinear_variables_constraints, equality_constraints, linear_constraints)
  • status: [OUT] Vector{Cint}
  • nonlinearvariablesobjective: [OUT] Vector{Cint}
  • nonlinearvariablesconstraints: [OUT] Vector{Cint}
  • equality_constraints: [OUT] Vector{Cint}
  • linear_constraints: [OUT] Vector{Cint}
source
CUTEst.cnames — Function
cnames(T, libsif, status, n, m, pname, vname, cname)

The cnames subroutine obtains the names of the problem, its variables and general constraints. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • pname: [OUT] Vector{Cchar}
  • vname: [OUT] Vector{Cchar}
  • cname: [OUT] Vector{Cchar}

To get useful names, use String(x) where x can be pname, vname[:,i], or cname[:,i].

source
CUTEst.ugrsh — Function
ugrsh(T, libsif, status, n, x, g, nnzh, lh, h_val, h_row, h_col)

The ugrsh subroutine evaluates the gradient and Hessian matrix of the objective function of the problem decoded from a SIF file by the script sifdecoder at the point X. This Hessian matrix is stored as a sparse matrix in coordinate format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group- partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • g: [OUT] Vector{T}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_val: [OUT] Vector{T}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.varnames — Function
varnames(T, libsif, status, n, vname)

The varnames subroutine obtains the names of the problem variables. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • vname: [OUT] Vector{Cchar}

To get useful names, use String(vname[:, i]).

source
CUTEst.uvartype — Function
uvartype(T, libsif, status, n, x_type)

The uvartype subroutine determines the type (continuous, 0-1, integer) of each variable involved in the problem decoded from a SIF file by the script sifdecoder. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to the simple bounds xl≤x≤xu. The objective function is group-partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • x_type: [OUT] Vector{Cint}
source
CUTEst.fopen — Function
fopen(T, libsif, funit, outsdif, status)
source
CUTEst.csgrshp — Function
csgrshp(T, libsif, status, n, nnzj, lj, j_var, j_fun, nnzh, lh, h_row, h_col)

The csgrshp subroutine evaluates sparsity pattern used when storing the gradients of the general constraints and of either the objective function or the Lagrangian function l(x,y)=f(x)+yTc(x), as well as the Hessian of the Lagrangian function, corresponding to the problem decoded from a SIF file by the script sifdecoder.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • j_var: [OUT] Vector{Cint}
  • j_fun: [OUT] Vector{Cint}
  • nnzh: [OUT] Vector{Cint}
  • lh: [IN] Vector{Cint}
  • h_row: [OUT] Vector{Cint}
  • h_col: [OUT] Vector{Cint}
source
CUTEst.ccfsg — Function
ccfsg(T, libsif, status, n, m, x, c, nnzj, lj, j_val, j_var, j_fun, grad)

The ccfsg subroutine evaluates the values of the constraint functions of the problem decoded from a SIF file by the script sifdecoder at the point X, and possibly their gradients in the constrained minimization case. The gradients are stored in sparse format. The problem under consideration is to minimize or maximize an objective function f(x) over all x ∈ Rn subject to general equations ci(x)=0, (i ∈ 1,...,mE), general inequalities ci(x)≤ci(x)≤ci(x), (i ∈ mE+1,...,m), and simple bounds xl≤x≤xu. The objective function is group-partially separable and all constraint functions are partially separable.

  • status: [OUT] Vector{Cint}
  • n: [IN] Vector{Cint}
  • m: [IN] Vector{Cint}
  • x: [IN] Vector{T}
  • c: [OUT] Vector{T}
  • nnzj: [OUT] Vector{Cint}
  • lj: [IN] Vector{Cint}
  • j_val: [OUT] Vector{T}
  • j_var: [OUT] Vector{Cint}
  • j_fun: [OUT] Vector{Cint}
  • grad: [IN] Vector{Bool}
source
CUTEst.classification — Function
classification(T, libsif, status, funit, class)
source
diff --git a/dev/index.html b/dev/index.html index 1c3c61b..6781bb7 100644 --- a/dev/index.html +++ b/dev/index.html @@ -18,4 +18,4 @@ end
nh = 50, nnzh = 153
 nh = 100, nnzh = 303
 nh = 150, nnzh = 1203
-nh = 200, nnzh = 603

finalize(nlp) is automatically handled by the garbage collector; however, you may need to call it manually if you want to recreate a model with the same problem but of a different size.

Working with CUTEst directly

We have also implemented functions to allow direct access to CUTEst functions through a core API, which serves as a wrapper for CUTEst. For more information, see the section on the core interface.

+nh = 200, nnzh = 603

finalize(nlp) is automatically handled by the garbage collector; however, you may need to call it manually if you want to recreate a model with the same problem but of a different size.

Working with CUTEst directly

We have also implemented functions to allow direct access to CUTEst functions through a core API, which serves as a wrapper for CUTEst. For more information, see the section on the core interface.

diff --git a/dev/model/index.html b/dev/model/index.html index d6f3a92..5e1f923 100644 --- a/dev/model/index.html +++ b/dev/model/index.html @@ -11,4 +11,4 @@ # Create another CUTEstModel with different parameters nlp = CUTEstModel{Float64}("CHAIN", "-param", "NH=100") display(nlp) -finalize(nlp) # Finalize the new modelsource +finalize(nlp) # Finalize the new modelsource diff --git a/dev/reference/index.html b/dev/reference/index.html index 4730af5..bfef4f8 100644 --- a/dev/reference/index.html +++ b/dev/reference/index.html @@ -1,2 +1,2 @@ -Reference · CUTEst.jl
GitHub

Reference

Index

CUTEst.cons_coord — Function
cons_coord(nlp, x)

Computes the constraint vector and the Jacobian in coordinate format. Usage:

c, jrow, jcol, jval = cons_coord(nlp, x)
  • nlp: [IN] CUTEstModel
  • x: [IN] Vector{T}
  • c: [OUT] Vector{T}
  • jrow: [OUT] Vector{Cint}
  • jcol: [OUT] Vector{Cint}
  • jval: [OUT] Vector{T}
source
CUTEst.cons_coord! — Function
cons_coord!(nlp, x, c, rows, cols, vals)

Computes the constraint vector and the Jacobian in coordinate format. Usage:

cons_coord!(nlp, x, c, rows, cols, vals)
  • nlp: [IN] CUTEstModel
  • x: [IN] Vector{T}
  • c: [OUT] Vector{T}
  • jrow: [OUT] Vector{Cint}
  • jcol: [OUT] Vector{Cint}
  • jval: [OUT] Vector{T}
source
CUTEst.consjac — Function
consjac(nlp, x)

Computes the constraint vector and the Jacobian in internal sparse format. Usage:

c, J = consjac(nlp, x)
  • nlp: [IN] CUTEstModel
  • x: [IN] Vector{T}
  • c: [OUT] Vector{T}
  • J: [OUT] Base.SparseMatrix.SparseMatrixCSC{T,Cint}
source
+Reference · CUTEst.jl
GitHub

Reference

Index

CUTEst.cons_coord — Function
cons_coord(nlp, x)

Computes the constraint vector and the Jacobian in coordinate format. Usage:

c, jrow, jcol, jval = cons_coord(nlp, x)
  • nlp: [IN] CUTEstModel
  • x: [IN] Vector{T}
  • c: [OUT] Vector{T}
  • jrow: [OUT] Vector{Cint}
  • jcol: [OUT] Vector{Cint}
  • jval: [OUT] Vector{T}
source
CUTEst.cons_coord! — Function
cons_coord!(nlp, x, c, rows, cols, vals)

Computes the constraint vector and the Jacobian in coordinate format. Usage:

cons_coord!(nlp, x, c, rows, cols, vals)
  • nlp: [IN] CUTEstModel
  • x: [IN] Vector{T}
  • c: [OUT] Vector{T}
  • jrow: [OUT] Vector{Cint}
  • jcol: [OUT] Vector{Cint}
  • jval: [OUT] Vector{T}
source
CUTEst.consjac — Function
consjac(nlp, x)

Computes the constraint vector and the Jacobian in internal sparse format. Usage:

c, J = consjac(nlp, x)
  • nlp: [IN] CUTEstModel
  • x: [IN] Vector{T}
  • c: [OUT] Vector{T}
  • J: [OUT] Base.SparseMatrix.SparseMatrixCSC{T,Cint}
source
diff --git a/dev/sifdecoder/index.html b/dev/sifdecoder/index.html index 666a724..531a8d6 100644 --- a/dev/sifdecoder/index.html +++ b/dev/sifdecoder/index.html @@ -1,7 +1,7 @@ -Managing SIF files · CUTEst.jl
GitHub

CUTEst.jl relies on SIF files to create a CUTEstModel. Three sets of SIF files are available on Bitbucket. We can easily switch between these sets using the function set_mastsif.

CUTEst.set_mastsif — Function
set_mastsif(set::String="sifcollection")

Set the environment variable MASTSIF to point to a set of SIF problems. The supported sets are:

  • "sifcollection": the CUTEst NLP test set;
  • "maros-meszaros": the Maros-Meszaros QP test set;
  • "netlib-lp": the Netlib LP test set.
source
Note

If the environment variable MASTSIF is not set, using CUTEst will automatically load the set "sifcollection".

To have the list of SIF problems available in the current set, use the function list_sif_problems.

CUTEst.list_sif_problems — Function
problems = list_sif_problems()

Output

  • problems::Vector{String}: A list of SIF problems (without the extension .SIF) available in the current set.

See set_mastsif for more details about how to change the set of SIF files.

source

Given a problem name in the defined set, we need to decode the SIF files, which will generate corresponding Fortran files.

CUTEst.sifdecoder — Function
sifdecoder(name::String, args...; verbose::Bool=false, precision::Symbol=:double,
+Managing SIF files · CUTEst.jl
GitHub
CUTEst.jl relies on SIF files to create a CUTEstModel. Three sets of SIF files are available on Bitbucket. We can easily switch between these sets using the function set_mastsif.
CUTEst.set_mastsif — Function
set_mastsif(set::String="sifcollection")
Set the environment variable MASTSIF to point to a set of SIF problems. The supported sets are:
  • "sifcollection": the CUTEst NLP test set;
  • "maros-meszaros": the Maros-Meszaros QP test set;
  • "netlib-lp": the Netlib LP test set.
source
Note
If the environment variable MASTSIF is not set, using CUTEst will automatically load the set "sifcollection".
To have the list of SIF problems available in the current set, use the function list_sif_problems.
CUTEst.list_sif_problems — Function
problems = list_sif_problems()
Output
  • problems::Vector{String}: A list of SIF problems (without the extension .SIF) available in the current set.
See set_mastsif for more details about how to change the set of SIF files.
source
Given a problem name in the defined set, we need to decode the SIF files, which will generate corresponding Fortran files.
CUTEst.sifdecoder — Function
sifdecoder(name::String, args...; verbose::Bool=false, precision::Symbol=:double,
            outsdif::String=_name_outsdif(name, precision), libsif_folder=libsif_path)
Decodes a SIF problem, converting it into a format suitable for further processing.
Arguments
  • name::String: The path or name of the SIF problem, with or without the extension .SIF.
  • args...: Additional arguments passed directly to the SIF decoder. Relevant for problems with variable sizes.
  • verbose::Bool: If true, enables verbose output during the decoding process. Defaults to false.
  • precision::Symbol: The desired precision for the problem. Can be :single, :double (default), or :quadruple.
  • outsdif::String: The name of the file OUTSDIF.d required for automatic differentiation. Defaults to "OUTSDIF_sifname_precision.d", where sifname and precision are replaced with the problem name and chosen precision.
  • libsif_folder::String: The directory where the generated files (*.f and *.d) will be stored. Defaults to libsif_path.
sifdecoder("HS1.SIF", precision=:single)
 sifdecoder("DIXMAANJ", "-param", "M=30"; precision=:double, verbose=true)
-sifdecoder("/home/alexis/CUTEst/BROWNDEN.SIF", precision=:quadruple)
source
Next, we need to compile these Fortran files and create a shared library linked with a CUTEst library for a given precision (single, double, or quadruple). The CUTEst library provides generic routines to evaluate objectives, constraints, gradients, Jacobians, Hessians, etc.
CUTEst.build_libsif — Function
build_libsif(name::String; precision::Symbol=:double, libsif_folder::String=libsif_path)
Builds a shared library from a decoded SIF problem.
Arguments
  • name::String: The path or name of the SIF problem, with or without the extension .SIF.
  • precision::Symbol: The desired precision of the problem. Can be :single, :double (default), or :quadruple.
  • libsif_folder::String: The directory where the compiled library will be stored. Defaults to libsif_path.
Warning
We expect that the SIF problem has been decoded in libsif_folder and contains the generated files (*.f and *.d).
build_libsif("HS1.SIF", precision=:single)
+sifdecoder("/home/alexis/CUTEst/BROWNDEN.SIF", precision=:quadruple)
source
Next, we need to compile these Fortran files and create a shared library linked with a CUTEst library for a given precision (single, double, or quadruple). The CUTEst library provides generic routines to evaluate objectives, constraints, gradients, Jacobians, Hessians, etc.
CUTEst.build_libsif — Function
build_libsif(name::String; precision::Symbol=:double, libsif_folder::String=libsif_path)
Builds a shared library from a decoded SIF problem.
Arguments
  • name::String: The path or name of the SIF problem, with or without the extension .SIF.
  • precision::Symbol: The desired precision of the problem. Can be :single, :double (default), or :quadruple.
  • libsif_folder::String: The directory where the compiled library will be stored. Defaults to libsif_path.
Warning
We expect that the SIF problem has been decoded in libsif_folder and contains the generated files (*.f and *.d).
build_libsif("HS1.SIF", precision=:single)
 build_libsif("DIXMAANJ", precision=:double)
-build_libsif("/home/alexis/CUTEst/BROWNDEN.SIF", precision=:quadruple)
source
Note
When creating a CUTEstModel, the functions sifdecoder and build_libsif are called automatically.
These additional files are generated in the folder deps/files at the root of the installation directory of CUTEst.jl . The path to this folder is available via CUTEst.libsif_path. We provide two functions to delete these files:
CUTEst.manage_libsif — Function
manage_libsif(; sort_by::Symbol=:name, rev::Bool=false)
Opens a prompt allowing the user to selectively remove compiled libraries for SIF problems. Data files OUTSDIF_*.d, which store preprocessed information required for automatic differentiation within CUTEst, are also removed.
By default, the problems are sorted by name. Alternatively, you can sort them by file size on disk by specifying sort_by=:size. Use rev=true to reverse the sort order.
source
CUTEst.clear_libsif — Function
clear_libsif()
Removes all compiled libraries and data files associated with SIF problems.
source

+build_libsif("/home/alexis/CUTEst/BROWNDEN.SIF", precision=:quadruple)
source
Note

When creating a CUTEstModel, the functions sifdecoder and build_libsif are called automatically.

These additional files are generated in the folder deps/files at the root of the installation directory of CUTEst.jl . The path to this folder is available via CUTEst.libsif_path. We provide two functions to delete these files:

CUTEst.manage_libsif — Function
manage_libsif(; sort_by::Symbol=:name, rev::Bool=false)

Opens a prompt allowing the user to selectively remove compiled libraries for SIF problems. Data files OUTSDIF_*.d, which store preprocessed information required for automatic differentiation within CUTEst, are also removed.

By default, the problems are sorted by name. Alternatively, you can sort them by file size on disk by specifying sort_by=:size. Use rev=true to reverse the sort order.

source
CUTEst.clear_libsif — Function
clear_libsif()

Removes all compiled libraries and data files associated with SIF problems.

source
diff --git a/dev/tutorial/index.html b/dev/tutorial/index.html index fbcec51..b691b30 100644 --- a/dev/tutorial/index.html +++ b/dev/tutorial/index.html @@ -1,2 +1,2 @@ -Overview of CUTEst.jl · CUTEst.jl
GitHub
+Overview of CUTEst.jl · CUTEst.jl
GitHub