Reference

Contents

Index

NLPModels.CountersType
Counters

Struct for storing the number of function evaluations.


Counters()

Creates an empty Counters struct.

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NLPModels.bound_constrainedMethod
bound_constrained(nlp)
bound_constrained(meta)

Returns whether the problem has bounds on the variables and no other constraints.

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NLPModels.coo_prod!Method
coo_prod!(rows, cols, vals, v, Av)

Compute the product of a matrix A given by (rows, cols, vals) and the vector v. The result is stored in Av, which should have length equals to the number of rows of A.

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NLPModels.coo_sym_prod!Method
coo_sym_prod!(rows, cols, vals, v, Av)

Compute the product of a symmetric matrix A given by (rows, cols, vals) and the vector v. The result is stored in Av, which should have length equals to the number of rows of A. Only one triangle of A should be passed.

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NLPModels.equality_constrainedMethod
equality_constrained(nlp)
equality_constrained(meta)

Returns whether the problem's constraints are all equalities. Unconstrained problems return false.

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NLPModels.get_linMethod
get_lin(nls)
get_lin(nls_meta)

Return the value lin from nls_meta or nls.nls_meta.

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NLPModels.get_nlnMethod
get_nln(nls)
get_nln(nls_meta)

Return the value nln from nls_meta or nls.nls_meta.

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NLPModels.get_x0Method
get_x0(nls)
get_x0(nls_meta)

Return the value x0 from nls_meta or nls.nls_meta.

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NLPModels.ghjvprodMethod

gHv = ghjvprod(nlp, x, g, v)

Return the vector whose i-th component is gᵀ ∇²cᵢ(x) v.

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NLPModels.gradMethod
g = grad(nlp, x)

Evaluate $∇f(x)$, the gradient of the objective function at x.

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NLPModels.has_equalitiesMethod
has_equalities(nlp)

Returns whether the problem has constraints and at least one of them is an equality. Unconstrained problems return false.

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NLPModels.has_inequalitiesMethod
has_inequalities(nlp)

Returns whether the problem has constraints and at least one of them is an inequality. Unconstrained problems return false.

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NLPModels.hessMethod
Hx = hess(nlp, x, y; obj_weight=1.0)

Evaluate the Lagrangian Hessian at (x,y) as a sparse matrix, with objective function scaled by obj_weight, i.e.,

\[∇²L(x,y) = σ ∇²f(x) + \sum_i yᵢ ∇²cᵢ(x),\]

with σ = obj_weight . A Symmetric object wrapping the lower triangle is returned.

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NLPModels.hessMethod
Hx = hess(nlp, x; obj_weight=1.0)

Evaluate the objective Hessian at x as a sparse matrix, with objective function scaled by obj_weight, i.e.,

\[σ ∇²f(x),\]

with σ = obj_weight . A Symmetric object wrapping the lower triangle is returned.

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NLPModels.hess_coord!Method
vals = hess_coord!(nlp, x, vals; obj_weight=1.0)

Evaluate the objective Hessian at x in sparse coordinate format, with objective function scaled by obj_weight, i.e.,

\[σ ∇²f(x),\]

with σ = obj_weight , overwriting vals. Only the lower triangle is returned.

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NLPModels.hess_coordMethod
vals = hess_coord(nlp, x, y; obj_weight=1.0)

Evaluate the Lagrangian Hessian at (x,y) in sparse coordinate format, with objective function scaled by obj_weight, i.e.,

\[∇²L(x,y) = σ ∇²f(x) + \sum_i yᵢ ∇²cᵢ(x),\]

with σ = obj_weight . Only the lower triangle is returned.

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NLPModels.hess_coordMethod
vals = hess_coord(nlp, x; obj_weight=1.0)

Evaluate the objective Hessian at x in sparse coordinate format, with objective function scaled by obj_weight, i.e.,

\[σ ∇²f(x),\]

with σ = obj_weight . Only the lower triangle is returned.

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NLPModels.hess_coord_residualMethod
vals = hess_coord_residual(nls, x, v)

Computes the linear combination of the Hessians of the residuals at x with coefficients v in sparse coordinate format.

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NLPModels.hess_op!Method
H = hess_op!(nlp, x, y, Hv; obj_weight=1.0)

Return the Lagrangian Hessian at (x,y) with objective function scaled by obj_weight as a linear operator, and storing the result on Hv. The resulting object may be used as if it were a matrix, e.g., w = H * v. The vector Hv is used as preallocated storage for the operation. The linear operator H represents

\[∇²L(x,y) = σ ∇²f(x) + \sum_i yᵢ ∇²cᵢ(x),\]

with σ = obj_weight .

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NLPModels.hess_op!Method
H = hess_op!(nlp, x, Hv; obj_weight=1.0)

Return the objective Hessian at x with objective function scaled by obj_weight as a linear operator, and storing the result on Hv. The resulting object may be used as if it were a matrix, e.g., w = H * v. The vector Hv is used as preallocated storage for the operation. The linear operator H represents

\[σ ∇²f(x),\]

with σ = obj_weight .

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NLPModels.hess_op!Method
H = hess_op!(nlp, rows, cols, vals, Hv)

Return the Hessian given by (rows, cols, vals) as a linear operator, and storing the result on Hv. The resulting object may be used as if it were a matrix, e.g., w = H * v. The vector Hv is used as preallocated storage for the operation. The linear operator H represents

\[σ ∇²f(x),\]

with σ = obj_weight .

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NLPModels.hess_opMethod
H = hess_op(nlp, x, y; obj_weight=1.0)

Return the Lagrangian Hessian at (x,y) with objective function scaled by obj_weight as a linear operator. The resulting object may be used as if it were a matrix, e.g., H * v. The linear operator H represents

\[∇²L(x,y) = σ ∇²f(x) + \sum_i yᵢ ∇²cᵢ(x),\]

with σ = obj_weight .

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NLPModels.hess_opMethod
H = hess_op(nlp, x; obj_weight=1.0)

Return the objective Hessian at x with objective function scaled by obj_weight as a linear operator. The resulting object may be used as if it were a matrix, e.g., H * v. The linear operator H represents

\[σ ∇²f(x),\]

with σ = obj_weight .

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NLPModels.hess_op_residual!Method
Hop = hess_op_residual!(nls, x, i, Hiv)

Computes the Hessian of the i-th residual at x, in linear operator form. The vector Hiv is used as preallocated storage for the operation.

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NLPModels.hess_residualMethod
H = hess_residual(nls, x, v)

Computes the linear combination of the Hessians of the residuals at x with coefficients v. A Symmetric object wrapping the lower triangle is returned.

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NLPModels.histlineMethod
histline(s, v, maxv)

Return a string of the form

______NAME______: ████⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 5

where:

  • ______NAME______ is s with padding to the left and length 16.
  • And the symbols █ and ⋅ fill 20 characters in the proportion of v / maxv to █ and the rest to ⋅.
  • The number 5 is v.
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NLPModels.hprod!Method
Hv = hprod!(nlp, rows, cols, vals, v, Hv)

Evaluate the product of the objective or Lagrangian Hessian given by (rows, cols, vals) in triplet format with the vector v in place. Only one triangle of the Hessian should be given.

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NLPModels.hprod!Method
Hv = hprod!(nlp, x, v, Hv; obj_weight=1.0)

Evaluate the product of the objective Hessian at x with the vector v in place, with objective function scaled by obj_weight, where the objective Hessian is

\[σ ∇²f(x),\]

with σ = obj_weight .

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NLPModels.hprodMethod
Hv = hprod(nlp, x, y, v; obj_weight=1.0)

Evaluate the product of the Lagrangian Hessian at (x,y) with the vector v, with objective function scaled by obj_weight, where the Lagrangian Hessian is

\[∇²L(x,y) = σ ∇²f(x) + \sum_i yᵢ ∇²cᵢ(x),\]

with σ = obj_weight .

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NLPModels.hprodMethod
Hv = hprod(nlp, x, v; obj_weight=1.0)

Evaluate the product of the objective Hessian at x with the vector v, with objective function scaled by obj_weight, where the objective Hessian is

\[σ ∇²f(x),\]

with σ = obj_weight .

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NLPModels.hprod_residualMethod
Hiv = hprod_residual(nls, x, i, v)

Computes the product of the Hessian of the i-th residual at x, times the vector v.

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NLPModels.inequality_constrainedMethod
inequality_constrained(nlp)
inequality_constrained(meta)

Returns whether the problem's constraints are all inequalities. Unconstrained problems return true.

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NLPModels.jacMethod
Jx = jac(nlp, x)

Evaluate $J(x)$, the constraints Jacobian at x as a sparse matrix.

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NLPModels.jac_coord!Method
vals = jac_coord!(nlp, x, vals)

Evaluate $J(x)$, the constraints Jacobian at x in sparse coordinate format, rewriting vals.

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NLPModels.jac_coordMethod
vals = jac_coord(nlp, x)

Evaluate $J(x)$, the constraints Jacobian at x in sparse coordinate format.

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NLPModels.jac_linMethod
Jx = jac_lin(nlp, x)

Evaluate $J(x)$, the linear constraints Jacobian at x as a sparse matrix.

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NLPModels.jac_lin_coordMethod
vals = jac_lin_coord(nlp, x)

Evaluate $J(x)$, the linear constraints Jacobian at x in sparse coordinate format.

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NLPModels.jac_lin_op!Method
J = jac_lin_op!(nlp, rows, cols, vals, Jv, Jtv)

Return the linear Jacobian given by (rows, cols, vals) as a linear operator. The resulting object may be used as if it were a matrix, e.g., J * v or J' * v. The values Jv and Jtv are used as preallocated storage for the operations.

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NLPModels.jac_lin_op!Method
J = jac_lin_op!(nlp, x, Jv, Jtv)

Return the linear Jacobian at x as a linear operator. The resulting object may be used as if it were a matrix, e.g., J * v or J' * v. The values Jv and Jtv are used as preallocated storage for the operations.

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NLPModels.jac_lin_opMethod
J = jac_lin_op(nlp, x)

Return the linear Jacobian at x as a linear operator. The resulting object may be used as if it were a matrix, e.g., J * v or J' * v.

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NLPModels.jac_nlnMethod
Jx = jac_nln(nlp, x)

Evaluate $J(x)$, the nonlinear constraints Jacobian at x as a sparse matrix.

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NLPModels.jac_nln_coordMethod
vals = jac_nln_coord(nlp, x)

Evaluate $J(x)$, the nonlinear constraints Jacobian at x in sparse coordinate format.

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NLPModels.jac_nln_op!Method
J = jac_nln_op!(nlp, rows, cols, vals, Jv, Jtv)

Return the nonlinear Jacobian given by (rows, cols, vals) as a linear operator. The resulting object may be used as if it were a matrix, e.g., J * v or J' * v. The values Jv and Jtv are used as preallocated storage for the operations.

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NLPModels.jac_nln_op!Method
J = jac_nln_op!(nlp, x, Jv, Jtv)

Return the nonlinear Jacobian at x as a linear operator. The resulting object may be used as if it were a matrix, e.g., J * v or J' * v. The values Jv and Jtv are used as preallocated storage for the operations.

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NLPModels.jac_nln_opMethod
J = jac_nln_op(nlp, x)

Return the nonlinear Jacobian at x as a linear operator. The resulting object may be used as if it were a matrix, e.g., J * v or J' * v.

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NLPModels.jac_nln_structureMethod
(rows,cols) = jac_nln_structure(nlp)

Return the structure of the nonlinear constraints Jacobian in sparse coordinate format.

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NLPModels.jac_op!Method
J = jac_op!(nlp, rows, cols, vals, Jv, Jtv)

Return the Jacobian given by (rows, cols, vals) as a linear operator. The resulting object may be used as if it were a matrix, e.g., J * v or J' * v. The values Jv and Jtv are used as preallocated storage for the operations.

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NLPModels.jac_op!Method
J = jac_op!(nlp, x, Jv, Jtv)

Return the Jacobian at x as a linear operator. The resulting object may be used as if it were a matrix, e.g., J * v or J' * v. The values Jv and Jtv are used as preallocated storage for the operations.

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NLPModels.jac_opMethod
J = jac_op(nlp, x)

Return the Jacobian at x as a linear operator. The resulting object may be used as if it were a matrix, e.g., J * v or J' * v.

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NLPModels.jac_op_residual!Method
Jx = jac_op_residual!(nls, x, Jv, Jtv)

Computes $J(x)$, the Jacobian of the residual at x, in linear operator form. The vectors Jv and Jtv are used as preallocated storage for the operations.

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NLPModels.jac_op_residual!Method
Jx = jac_op_residual!(nls, rows, cols, vals, Jv, Jtv)

Computes $J(x)$, the Jacobian of the residual given by (rows, cols, vals), in linear operator form. The vectors Jv and Jtv are used as preallocated storage for the operations.

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NLPModels.jac_structure!Method
jac_structure!(nlp, rows, cols)

Return the structure of the constraints Jacobian in sparse coordinate format in place.

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NLPModels.jac_structureMethod
(rows,cols) = jac_structure(nlp)

Return the structure of the constraints Jacobian in sparse coordinate format.

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NLPModels.jprod!Method
Jv = jprod!(nlp, x, v, Jv)

Evaluate $J(x)v$, the Jacobian-vector product at x in place.

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NLPModels.jprod!Method
Jv = jprod!(nlp, rows, cols, vals, v, Jv)

Evaluate $J(x)v$, the Jacobian-vector product, where the Jacobian is given by (rows, cols, vals) in triplet format.

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NLPModels.jprod_lin!Method
Jv = jprod_lin!(nlp, rows, cols, vals, v, Jv)

Evaluate $J(x)v$, the linear Jacobian-vector product, where the Jacobian is given by (rows, cols, vals) in triplet format.

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NLPModels.jprod_nln!Method
Jv = jprod_nln!(nlp, rows, cols, vals, v, Jv)

Evaluate $J(x)v$, the nonlinear Jacobian-vector product, where the Jacobian is given by (rows, cols, vals) in triplet format.

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NLPModels.jprod_residual!Method
Jv = jprod_residual!(nls, rows, cols, vals, v, Jv)

Computes the product of the Jacobian of the residual given by (rows, cols, vals) and a vector, i.e., $J(x)v$, storing it in Jv.

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NLPModels.jprod_residualMethod
Jv = jprod_residual(nls, x, v)

Computes the product of the Jacobian of the residual at x and a vector, i.e., $J(x)v$.

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NLPModels.jth_hessMethod

Hx = jth_hess(nlp, x, j)

Evaluate the Hessian of j-th constraint at x as a sparse matrix with the same sparsity pattern as the Lagrangian Hessian. A Symmetric object wrapping the lower triangle is returned.

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NLPModels.jth_hess_coordMethod
vals = jth_hess_coord(nlp, x, j)

Evaluate the Hessian of j-th constraint at x in sparse coordinate format. Only the lower triangle is returned.

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NLPModels.jth_hprodMethod
Hv = jth_hprod(nlp, x, v, j)

Evaluate the product of the Hessian of j-th constraint at x with the vector v.

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NLPModels.jtprod!Method
Jtv = jtprod!(nlp, x, v, Jtv)

Evaluate $J(x)^Tv$, the transposed-Jacobian-vector product at x in place. If the problem has linear and nonlinear constraints, this function allocates.

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NLPModels.jtprod!Method
Jtv = jtprod!(nlp, rows, cols, vals, v, Jtv)

Evaluate $J(x)^Tv$, the transposed-Jacobian-vector product, where the Jacobian is given by (rows, cols, vals) in triplet format.

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NLPModels.jtprodMethod
Jtv = jtprod(nlp, x, v, Jtv)

Evaluate $J(x)^Tv$, the transposed-Jacobian-vector product at x.

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NLPModels.jtprod_lin!Method
Jtv = jtprod_lin!(nlp, rows, cols, vals, v, Jtv)

Evaluate $J(x)^Tv$, the linear transposed-Jacobian-vector product, where the Jacobian is given by (rows, cols, vals) in triplet format.

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NLPModels.jtprod_linMethod
Jtv = jtprod_lin(nlp, x, v, Jtv)

Evaluate $J(x)^Tv$, the linear transposed-Jacobian-vector product at x.

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NLPModels.jtprod_nln!Method
Jtv = jtprod_nln!(nlp, rows, cols, vals, v, Jtv)

Evaluate $J(x)^Tv$, the nonlinear transposed-Jacobian-vector product, where the Jacobian is given by (rows, cols, vals) in triplet format.

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NLPModels.jtprod_nlnMethod
Jtv = jtprod_nln(nlp, x, v, Jtv)

Evaluate $J(x)^Tv$, the nonlinear transposed-Jacobian-vector product at x.

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NLPModels.jtprod_residual!Method
Jtv = jtprod_residual!(nls, rows, cols, vals, v, Jtv)

Computes the product of the transpose of the Jacobian of the residual given by (rows, cols, vals) and a vector, i.e., $J(x)^Tv$, storing it in Jv.

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NLPModels.jtprod_residualMethod
Jtv = jtprod_residual(nls, x, v)

Computes the product of the transpose of the Jacobian of the residual at x and a vector, i.e., $J(x)^Tv$.

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NLPModels.lines_of_histMethod
lines_of_hist(S, V)

Return a vector of histline(s, v, maxv)s using pairs of s in S and v in V. maxv is given by the maximum of V.

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NLPModels.nls_metaMethod
nls_meta(nls)

Returns the nls_meta structure of nls. Use this instead of nls.nls_meta to handle models that have internal models.

For basic models nls_meta(nls) is defined as nls.nls_meta, but composite models might not keep nls_meta themselves, so they might specialize it to something like nls.internal.nls_meta.

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NLPModels.objcons!Method
f = objcons!(nlp, x, c)

Evaluate $f(x)$ and $c(x)$ at x. c is overwritten with the value of $c(x)$.

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NLPModels.objgrad!Method
f, g = objgrad!(nlp, x, g)

Evaluate $f(x)$ and $∇f(x)$ at x. g is overwritten with the value of $∇f(x)$.

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NLPModels.reset_data!Method
reset_data!(nlp)

Reset model data if appropriate. This method should be overloaded if a subtype of AbstractNLPModel contains data that should be reset, such as a quasi-Newton linear operator.

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NLPModels.show_headerMethod
show_header(io, nlp)

Show a header for the specific nlp type. Should be imported and defined for every model implementing the NLPModels API.

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NLPModels.sparsitylineMethod
sparsityline(s, v, maxv)

Return a string of the form

______NAME______: ( 80.00% sparsity)   5

where:

  • ______NAME______ is s with padding to the left and length 16.
  • The sparsity value is given by v / maxv.
  • The number 5 is v.
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