Reference
Contents
Index
NLPModels.AbstractNLPModel
NLPModels.AbstractNLPModelMeta
NLPModels.AbstractNLSModel
NLPModels.Counters
NLPModels.DimensionError
NLPModels.NLPModelMeta
NLPModels.NLSCounters
NLPModels.NLSMeta
Base.eltype
LinearOperators.reset!
LinearOperators.reset!
NLPModels.bound_constrained
NLPModels.cons
NLPModels.cons!
NLPModels.cons_lin
NLPModels.cons_lin!
NLPModels.cons_nln
NLPModels.cons_nln!
NLPModels.coo_prod!
NLPModels.coo_sym_prod!
NLPModels.decrement!
NLPModels.equality_constrained
NLPModels.get_ifix
NLPModels.get_ifree
NLPModels.get_iinf
NLPModels.get_ilow
NLPModels.get_irng
NLPModels.get_islp
NLPModels.get_iupp
NLPModels.get_jfix
NLPModels.get_jfree
NLPModels.get_jinf
NLPModels.get_jlow
NLPModels.get_jrng
NLPModels.get_jupp
NLPModels.get_lcon
NLPModels.get_lin
NLPModels.get_lin
NLPModels.get_lin_nnzj
NLPModels.get_lvar
NLPModels.get_minimize
NLPModels.get_name
NLPModels.get_ncon
NLPModels.get_nequ
NLPModels.get_nlin
NLPModels.get_nlin
NLPModels.get_nln
NLPModels.get_nln
NLPModels.get_nln_nnzj
NLPModels.get_nlvb
NLPModels.get_nlvc
NLPModels.get_nlvo
NLPModels.get_nnln
NLPModels.get_nnln
NLPModels.get_nnzh
NLPModels.get_nnzh
NLPModels.get_nnzj
NLPModels.get_nnzj
NLPModels.get_nnzo
NLPModels.get_nvar
NLPModels.get_nvar
NLPModels.get_ucon
NLPModels.get_uvar
NLPModels.get_x0
NLPModels.get_x0
NLPModels.get_y0
NLPModels.ghjvprod
NLPModels.ghjvprod!
NLPModels.grad
NLPModels.grad!
NLPModels.grad!
NLPModels.has_bounds
NLPModels.has_equalities
NLPModels.has_inequalities
NLPModels.hess
NLPModels.hess
NLPModels.hess_coord
NLPModels.hess_coord
NLPModels.hess_coord!
NLPModels.hess_coord!
NLPModels.hess_coord_residual
NLPModels.hess_coord_residual!
NLPModels.hess_op
NLPModels.hess_op
NLPModels.hess_op!
NLPModels.hess_op!
NLPModels.hess_op!
NLPModels.hess_op_residual
NLPModels.hess_op_residual!
NLPModels.hess_residual
NLPModels.hess_structure
NLPModels.hess_structure!
NLPModels.hess_structure_residual
NLPModels.hess_structure_residual!
NLPModels.histline
NLPModels.hprod
NLPModels.hprod
NLPModels.hprod!
NLPModels.hprod!
NLPModels.hprod!
NLPModels.hprod_residual
NLPModels.hprod_residual!
NLPModels.increment!
NLPModels.increment!
NLPModels.inequality_constrained
NLPModels.jac
NLPModels.jac_coord
NLPModels.jac_coord!
NLPModels.jac_coord_residual
NLPModels.jac_coord_residual!
NLPModels.jac_lin
NLPModels.jac_lin_coord
NLPModels.jac_lin_coord!
NLPModels.jac_lin_op
NLPModels.jac_lin_op!
NLPModels.jac_lin_op!
NLPModels.jac_lin_structure
NLPModels.jac_lin_structure!
NLPModels.jac_nln
NLPModels.jac_nln_coord
NLPModels.jac_nln_coord!
NLPModels.jac_nln_op
NLPModels.jac_nln_op!
NLPModels.jac_nln_op!
NLPModels.jac_nln_structure
NLPModels.jac_nln_structure!
NLPModels.jac_op
NLPModels.jac_op!
NLPModels.jac_op!
NLPModels.jac_op_residual
NLPModels.jac_op_residual!
NLPModels.jac_op_residual!
NLPModels.jac_residual
NLPModels.jac_structure
NLPModels.jac_structure!
NLPModels.jac_structure_residual
NLPModels.jac_structure_residual!
NLPModels.jprod
NLPModels.jprod!
NLPModels.jprod!
NLPModels.jprod_lin
NLPModels.jprod_lin!
NLPModels.jprod_lin!
NLPModels.jprod_nln
NLPModels.jprod_nln!
NLPModels.jprod_nln!
NLPModels.jprod_residual
NLPModels.jprod_residual!
NLPModels.jprod_residual!
NLPModels.jth_hess
NLPModels.jth_hess_coord
NLPModels.jth_hess_coord!
NLPModels.jth_hess_residual
NLPModels.jth_hprod
NLPModels.jth_hprod!
NLPModels.jtprod
NLPModels.jtprod!
NLPModels.jtprod!
NLPModels.jtprod_lin
NLPModels.jtprod_lin!
NLPModels.jtprod_lin!
NLPModels.jtprod_nln
NLPModels.jtprod_nln!
NLPModels.jtprod_nln!
NLPModels.jtprod_residual
NLPModels.jtprod_residual!
NLPModels.jtprod_residual!
NLPModels.linearly_constrained
NLPModels.lines_of_description
NLPModels.lines_of_description
NLPModels.lines_of_hist
NLPModels.neval_cons
NLPModels.neval_cons_lin
NLPModels.neval_cons_nln
NLPModels.neval_grad
NLPModels.neval_hess
NLPModels.neval_hess_residual
NLPModels.neval_hprod
NLPModels.neval_hprod_residual
NLPModels.neval_jac
NLPModels.neval_jac_lin
NLPModels.neval_jac_nln
NLPModels.neval_jac_residual
NLPModels.neval_jcon
NLPModels.neval_jgrad
NLPModels.neval_jhess
NLPModels.neval_jhess_residual
NLPModels.neval_jhprod
NLPModels.neval_jprod
NLPModels.neval_jprod_lin
NLPModels.neval_jprod_nln
NLPModels.neval_jprod_residual
NLPModels.neval_jtprod
NLPModels.neval_jtprod_lin
NLPModels.neval_jtprod_nln
NLPModels.neval_jtprod_residual
NLPModels.neval_obj
NLPModels.neval_residual
NLPModels.nls_meta
NLPModels.obj
NLPModels.obj
NLPModels.objcons
NLPModels.objcons!
NLPModels.objgrad
NLPModels.objgrad!
NLPModels.objgrad!
NLPModels.reset_data!
NLPModels.residual
NLPModels.residual!
NLPModels.show_counters
NLPModels.show_header
NLPModels.sparsityline
NLPModels.sum_counters
NLPModels.sum_counters
NLPModels.unconstrained
NLPModels.@default_counters
NLPModels.@default_nlscounters
NLPModels.@lencheck
NLPModels.@rangecheck
NLPModels.AbstractNLPModel
— TypeAbstractNLPModel
Base type for an optimization model.
NLPModels.AbstractNLPModelMeta
— TypeAbstractNLPModelMeta
Base type for metadata related to an optimization model.
NLPModels.AbstractNLSModel
— TypeAbstractNLSModel <: AbstractNLPModel
Base type for a nonlinear least-squares model.
NLPModels.Counters
— TypeCounters
Struct for storing the number of function evaluations.
Counters()
Creates an empty Counters struct.
NLPModels.DimensionError
— TypeDimensionError <: Exception
DimensionError(name, dim_expected, dim_found)
Error for unexpected dimension. Output: "DimensionError: Input name
should have length dim_expected
not dim_found
"
NLPModels.NLPModelMeta
— TypeNLPModelMeta <: AbstractNLPModelMeta
A composite type that represents the main features of the optimization problem
optimize obj(x)
subject to lvar ≤ x ≤ uvar
lcon ≤ cons(x) ≤ ucon
where x
is an nvar
-dimensional vector, obj
is the real-valued objective function, cons
is the vector-valued constraint function, optimize
is either "minimize" or "maximize".
Here, lvar
, uvar
, lcon
and ucon
are vectors. Some of their components may be infinite to indicate that the corresponding bound or general constraint is not present.
NLPModelMeta(nvar::Integer; kwargs...)
NLPModelMeta(meta::AbstractNLPModelMeta; kwargs...)
Create an NLPModelMeta
with nvar
variables. Alternatively, create an NLPModelMeta
copy from another AbstractNLPModelMeta
. The following keyword arguments are accepted:
x0
: initial guesslvar
: vector of lower boundsuvar
: vector of upper boundsnlvb
: number of nonlinear variables in both objectives and constraintsnlvo
: number of nonlinear variables in objectives (includes nlvb)nlvc
: number of nonlinear variables in constraints (includes nlvb)ncon
: number of general constraintsy0
: initial Lagrange multiplierslcon
: vector of constraint lower boundsucon
: vector of constraint upper boundsnnzo
: number of nonzeros in the gradientnnzj
: number of elements needed to store the nonzeros in the sparse Jacobianlin_nnzj
: number of elements needed to store the nonzeros in the sparse Jacobian of linear constraintsnln_nnzj
: number of elements needed to store the nonzeros in the sparse Jacobian of nonlinear constraintsnnzh
: number of elements needed to store the nonzeros in the sparse Hessianlin
: indices of linear constraintsminimize
: true if optimize == minimizeislp
: true if the problem is a linear programname
: problem name
NLPModelMeta
also contains the following attributes, which are computed from the variables above:
nvar
: number of variablesifix
: indices of fixed variablesilow
: indices of variables with lower bound onlyiupp
: indices of variables with upper bound onlyirng
: indices of variables with lower and upper bound (range)ifree
: indices of free variablesiinf
: indices of visibly infeasible boundsjfix
: indices of equality constraintsjlow
: indices of constraints of the form c(x) ≥ cljupp
: indices of constraints of the form c(x) ≤ cujrng
: indices of constraints of the form cl ≤ c(x) ≤ cujfree
: indices of "free" constraints (there shouldn't be any)jinf
: indices of the visibly infeasible constraintsnlin
: number of linear constraintsnnln
: number of nonlinear general constraintsnln
: indices of nonlinear constraints
NLPModels.NLSCounters
— TypeNLSCounters
Struct for storing the number of functions evaluations for nonlinear least-squares models. NLSCounters also stores a Counters
instance named counters
.
NLSCounters()
Creates an empty NLSCounters struct.
NLPModels.NLSMeta
— TypeNLSMeta
Base type for metadata related to a nonlinear least-squares model.
NLSMeta(nequ, nvar; kwargs...)
Create a NLSMeta
with nequ
equations and nvar
variables. The following keyword arguments are accepted:
x0
: initial guessnnzj
: number of elements needed to store the nonzeros of the Jacobian of the residualnnzh
: number of elements needed to store the nonzeros of the sum of Hessians of the residualslin
: indices of linear residuals
NLSMeta
also contains the following attributes, which are computed from the variables above:
nequ
: size of the residualnvar
: number of variablesnln
: indices of nonlinear residualsnnln
: number of nonlinear general residualsnlin
: number of linear residuals
Base.eltype
— Methodeltype(nlp::AbstractNLPModel{T, S})
Element type of nlp.meta.x0
.
LinearOperators.reset!
— Methodreset!(nlp)
Reset evaluation count and model data (if appropriate) in nlp
.
LinearOperators.reset!
— Methodreset!(counters)
Reset evaluation counters
NLPModels.bound_constrained
— Methodbound_constrained(nlp)
bound_constrained(meta)
Returns whether the problem has bounds on the variables and no other constraints.
NLPModels.cons!
— Methodc = cons!(nlp, x, c)
Evaluate $c(x)$, the constraints at x
in place.
NLPModels.cons
— Methodc = cons(nlp, x)
Evaluate $c(x)$, the constraints at x
.
NLPModels.cons_lin!
— Functionc = cons_lin!(nlp, x, c)
Evaluate the linear constraints at x
in place.
NLPModels.cons_lin
— Methodc = cons_lin(nlp, x)
Evaluate the linear constraints at x
.
NLPModels.cons_nln!
— Functionc = cons_nln!(nlp, x, c)
Evaluate the nonlinear constraints at x
in place.
NLPModels.cons_nln
— Methodc = cons_nln(nlp, x)
Evaluate the nonlinear constraints at x
.
NLPModels.coo_prod!
— Methodcoo_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
.
NLPModels.coo_sym_prod!
— Methodcoo_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.
NLPModels.decrement!
— Methoddecrement!(nlp, s)
Decrement counter s
of problem nlp
.
NLPModels.equality_constrained
— Methodequality_constrained(nlp)
equality_constrained(meta)
Returns whether the problem's constraints are all equalities. Unconstrained problems return false.
NLPModels.get_ifix
— Methodget_ifix(nlp)
get_ifix(meta)
Return the value ifix from meta or nlp.meta.
NLPModels.get_ifree
— Methodget_ifree(nlp)
get_ifree(meta)
Return the value ifree from meta or nlp.meta.
NLPModels.get_iinf
— Methodget_iinf(nlp)
get_iinf(meta)
Return the value iinf from meta or nlp.meta.
NLPModels.get_ilow
— Methodget_ilow(nlp)
get_ilow(meta)
Return the value ilow from meta or nlp.meta.
NLPModels.get_irng
— Methodget_irng(nlp)
get_irng(meta)
Return the value irng from meta or nlp.meta.
NLPModels.get_islp
— Methodget_islp(nlp)
get_islp(meta)
Return the value islp from meta or nlp.meta.
NLPModels.get_iupp
— Methodget_iupp(nlp)
get_iupp(meta)
Return the value iupp from meta or nlp.meta.
NLPModels.get_jfix
— Methodget_jfix(nlp)
get_jfix(meta)
Return the value jfix from meta or nlp.meta.
NLPModels.get_jfree
— Methodget_jfree(nlp)
get_jfree(meta)
Return the value jfree from meta or nlp.meta.
NLPModels.get_jinf
— Methodget_jinf(nlp)
get_jinf(meta)
Return the value jinf from meta or nlp.meta.
NLPModels.get_jlow
— Methodget_jlow(nlp)
get_jlow(meta)
Return the value jlow from meta or nlp.meta.
NLPModels.get_jrng
— Methodget_jrng(nlp)
get_jrng(meta)
Return the value jrng from meta or nlp.meta.
NLPModels.get_jupp
— Methodget_jupp(nlp)
get_jupp(meta)
Return the value jupp from meta or nlp.meta.
NLPModels.get_lcon
— Methodget_lcon(nlp)
get_lcon(meta)
Return the value lcon from meta or nlp.meta.
NLPModels.get_lin
— Methodget_lin(nlp)
get_lin(meta)
Return the value lin from meta or nlp.meta.
NLPModels.get_lin
— Methodget_lin(nls)
get_lin(nls_meta)
Return the value lin from nls_meta or nls.nls_meta.
NLPModels.get_lin_nnzj
— Methodget_lin_nnzj(nlp)
get_lin_nnzj(meta)
Return the value lin_nnzj from meta or nlp.meta.
NLPModels.get_lvar
— Methodget_lvar(nlp)
get_lvar(meta)
Return the value lvar from meta or nlp.meta.
NLPModels.get_minimize
— Methodget_minimize(nlp)
get_minimize(meta)
Return the value minimize from meta or nlp.meta.
NLPModels.get_name
— Methodget_name(nlp)
get_name(meta)
Return the value name from meta or nlp.meta.
NLPModels.get_ncon
— Methodget_ncon(nlp)
get_ncon(meta)
Return the value ncon from meta or nlp.meta.
NLPModels.get_nequ
— Methodget_nequ(nls)
get_nequ(nls_meta)
Return the value nequ from nls_meta or nls.nls_meta.
NLPModels.get_nlin
— Methodget_nlin(nlp)
get_nlin(meta)
Return the value nlin from meta or nlp.meta.
NLPModels.get_nlin
— Methodget_nlin(nls)
get_nlin(nls_meta)
Return the value nlin from nls_meta or nls.nls_meta.
NLPModels.get_nln
— Methodget_nln(nlp)
get_nln(meta)
Return the value nln from meta or nlp.meta.
NLPModels.get_nln
— Methodget_nln(nls)
get_nln(nls_meta)
Return the value nln from nls_meta or nls.nls_meta.
NLPModels.get_nln_nnzj
— Methodget_nln_nnzj(nlp)
get_nln_nnzj(meta)
Return the value nln_nnzj from meta or nlp.meta.
NLPModels.get_nlvb
— Methodget_nlvb(nlp)
get_nlvb(meta)
Return the value nlvb from meta or nlp.meta.
NLPModels.get_nlvc
— Methodget_nlvc(nlp)
get_nlvc(meta)
Return the value nlvc from meta or nlp.meta.
NLPModels.get_nlvo
— Methodget_nlvo(nlp)
get_nlvo(meta)
Return the value nlvo from meta or nlp.meta.
NLPModels.get_nnln
— Methodget_nnln(nlp)
get_nnln(meta)
Return the value nnln from meta or nlp.meta.
NLPModels.get_nnln
— Methodget_nnln(nls)
get_nnln(nls_meta)
Return the value nnln from nls_meta or nls.nls_meta.
NLPModels.get_nnzh
— Methodget_nnzh(nlp)
get_nnzh(meta)
Return the value nnzh from meta or nlp.meta.
NLPModels.get_nnzh
— Methodget_nnzh(nls)
get_nnzh(nls_meta)
Return the value nnzh from nls_meta or nls.nls_meta.
NLPModels.get_nnzj
— Methodget_nnzj(nlp)
get_nnzj(meta)
Return the value nnzj from meta or nlp.meta.
NLPModels.get_nnzj
— Methodget_nnzj(nls)
get_nnzj(nls_meta)
Return the value nnzj from nls_meta or nls.nls_meta.
NLPModels.get_nnzo
— Methodget_nnzo(nlp)
get_nnzo(meta)
Return the value nnzo from meta or nlp.meta.
NLPModels.get_nvar
— Methodget_nvar(nlp)
get_nvar(meta)
Return the value nvar from meta or nlp.meta.
NLPModels.get_nvar
— Methodget_nvar(nls)
get_nvar(nls_meta)
Return the value nvar from nls_meta or nls.nls_meta.
NLPModels.get_ucon
— Methodget_ucon(nlp)
get_ucon(meta)
Return the value ucon from meta or nlp.meta.
NLPModels.get_uvar
— Methodget_uvar(nlp)
get_uvar(meta)
Return the value uvar from meta or nlp.meta.
NLPModels.get_x0
— Methodget_x0(nlp)
get_x0(meta)
Return the value x0 from meta or nlp.meta.
NLPModels.get_x0
— Methodget_x0(nls)
get_x0(nls_meta)
Return the value x0 from nls_meta or nls.nls_meta.
NLPModels.get_y0
— Methodget_y0(nlp)
get_y0(meta)
Return the value y0 from meta or nlp.meta.
NLPModels.ghjvprod!
— Functionghjvprod!(nlp, x, g, v, gHv)
Return the vector whose i-th component is gᵀ ∇²cᵢ(x) v in place.
NLPModels.ghjvprod
— MethodgHv = ghjvprod(nlp, x, g, v)
Return the vector whose i-th component is gᵀ ∇²cᵢ(x) v.
NLPModels.grad!
— Functiong = grad!(nlp, x, g)
Evaluate $∇f(x)$, the gradient of the objective function at x
in place.
NLPModels.grad!
— Methodg = grad!(nls, x, g)
g = grad!(nls, x, g, Fx; recompute::Bool=true)
Evaluate ∇f(x)
, the gradient of the objective function of nls::AbstractNLSModel
at x
in place. Fx
is overwritten with the value of the residual F(x)
. If recompute
is true
, then Fx
is updated with the residual at x
.
NLPModels.grad
— Methodg = grad(nlp, x)
Evaluate $∇f(x)$, the gradient of the objective function at x
.
NLPModels.has_bounds
— Methodhas_bounds(nlp)
has_bounds(meta)
Returns whether the problem has bounds on the variables.
NLPModels.has_equalities
— Methodhas_equalities(nlp)
Returns whether the problem has constraints and at least one of them is an equality. Unconstrained problems return false.
NLPModels.has_inequalities
— Methodhas_inequalities(nlp)
Returns whether the problem has constraints and at least one of them is an inequality. Unconstrained problems return false.
NLPModels.hess
— MethodHx = 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.
NLPModels.hess
— MethodHx = 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.
NLPModels.hess_coord!
— Functionvals = hess_coord!(nlp, x, y, vals; 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
, overwriting vals
. Only the lower triangle is returned.
NLPModels.hess_coord!
— Methodvals = 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.
NLPModels.hess_coord
— Methodvals = 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.
NLPModels.hess_coord
— Methodvals = 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.
NLPModels.hess_coord_residual!
— Functionvals = hess_coord_residual!(nls, x, v, vals)
Computes the linear combination of the Hessians of the residuals at x
with coefficients v
in sparse coordinate format, rewriting vals
.
NLPModels.hess_coord_residual
— Methodvals = 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.
NLPModels.hess_op!
— MethodH = 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
.
NLPModels.hess_op!
— MethodH = 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
.
NLPModels.hess_op!
— MethodH = 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
.
NLPModels.hess_op
— MethodH = 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
.
NLPModels.hess_op
— MethodH = 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
.
NLPModels.hess_op_residual!
— MethodHop = 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.
NLPModels.hess_op_residual
— MethodHop = hess_op_residual(nls, x, i)
Computes the Hessian of the i-th residual at x, in linear operator form.
NLPModels.hess_residual
— MethodH = 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.
NLPModels.hess_structure!
— Functionhess_structure!(nlp, rows, cols)
Return the structure of the Lagrangian Hessian in sparse coordinate format in place.
NLPModels.hess_structure
— Method(rows,cols) = hess_structure(nlp)
Return the structure of the Lagrangian Hessian in sparse coordinate format.
NLPModels.hess_structure_residual!
— Functionhess_structure_residual!(nls, rows, cols)
Returns the structure of the residual Hessian in place.
NLPModels.hess_structure_residual
— Method(rows,cols) = hess_structure_residual(nls)
Returns the structure of the residual Hessian.
NLPModels.histline
— Methodhistline(s, v, maxv)
Return a string of the form
______NAME______: ████⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 5
where:
______NAME______
iss
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.
NLPModels.hprod!
— FunctionHv = hprod!(nlp, x, y, v, Hv; obj_weight=1.0)
Evaluate the product of the Lagrangian Hessian at (x,y)
with the vector v
in place, 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
.
NLPModels.hprod!
— MethodHv = 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.
NLPModels.hprod!
— MethodHv = 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
.
NLPModels.hprod
— MethodHv = 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
.
NLPModels.hprod
— MethodHv = 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
.
NLPModels.hprod_residual!
— FunctionHiv = hprod_residual!(nls, x, i, v, Hiv)
Computes the product of the Hessian of the i-th residual at x, times the vector v, and stores it in vector Hiv.
NLPModels.hprod_residual
— MethodHiv = hprod_residual(nls, x, i, v)
Computes the product of the Hessian of the i-th residual at x, times the vector v.
NLPModels.increment!
— Methodincrement!(nlp, s)
Increment counter s
of problem nlp
.
NLPModels.increment!
— Methodincrement!(nls, s)
Increment counter s
of problem nls
.
NLPModels.inequality_constrained
— Methodinequality_constrained(nlp)
inequality_constrained(meta)
Returns whether the problem's constraints are all inequalities. Unconstrained problems return true.
NLPModels.jac
— MethodJx = jac(nlp, x)
Evaluate $J(x)$, the constraints Jacobian at x
as a sparse matrix.
NLPModels.jac_coord!
— Methodvals = jac_coord!(nlp, x, vals)
Evaluate $J(x)$, the constraints Jacobian at x
in sparse coordinate format, rewriting vals
.
NLPModels.jac_coord
— Methodvals = jac_coord(nlp, x)
Evaluate $J(x)$, the constraints Jacobian at x
in sparse coordinate format.
NLPModels.jac_coord_residual!
— Functionvals = jac_coord_residual!(nls, x, vals)
Computes the Jacobian of the residual at x
in sparse coordinate format, rewriting vals
. rows
and cols
are not rewritten.
NLPModels.jac_coord_residual
— Method(rows,cols,vals) = jac_coord_residual(nls, x)
Computes the Jacobian of the residual at x
in sparse coordinate format.
NLPModels.jac_lin
— MethodJx = jac_lin(nlp, x)
Evaluate $J(x)$, the linear constraints Jacobian at x
as a sparse matrix.
NLPModels.jac_lin_coord!
— Functionvals = jac_lin_coord!(nlp, x, vals)
Evaluate $J(x)$, the linear constraints Jacobian at x
in sparse coordinate format, overwriting vals
.
NLPModels.jac_lin_coord
— Methodvals = jac_lin_coord(nlp, x)
Evaluate $J(x)$, the linear constraints Jacobian at x
in sparse coordinate format.
NLPModels.jac_lin_op!
— MethodJ = 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.
NLPModels.jac_lin_op!
— MethodJ = 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.
NLPModels.jac_lin_op
— MethodJ = 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
.
NLPModels.jac_lin_structure!
— Functionjac_lin_structure!(nlp, rows, cols)
Return the structure of the linear constraints Jacobian in sparse coordinate format in place.
NLPModels.jac_lin_structure
— Method(rows,cols) = jac_lin_structure(nlp)
Return the structure of the linear constraints Jacobian in sparse coordinate format.
NLPModels.jac_nln
— MethodJx = jac_nln(nlp, x)
Evaluate $J(x)$, the nonlinear constraints Jacobian at x
as a sparse matrix.
NLPModels.jac_nln_coord!
— Functionvals = jac_nln_coord!(nlp, x, vals)
Evaluate $J(x)$, the nonlinear constraints Jacobian at x
in sparse coordinate format, overwriting vals
.
NLPModels.jac_nln_coord
— Methodvals = jac_nln_coord(nlp, x)
Evaluate $J(x)$, the nonlinear constraints Jacobian at x
in sparse coordinate format.
NLPModels.jac_nln_op!
— MethodJ = 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.
NLPModels.jac_nln_op!
— MethodJ = 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.
NLPModels.jac_nln_op
— MethodJ = 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
.
NLPModels.jac_nln_structure!
— Functionjac_nln_structure!(nlp, rows, cols)
Return the structure of the nonlinear constraints Jacobian in sparse coordinate format in place.
NLPModels.jac_nln_structure
— Method(rows,cols) = jac_nln_structure(nlp)
Return the structure of the nonlinear constraints Jacobian in sparse coordinate format.
NLPModels.jac_op!
— MethodJ = 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.
NLPModels.jac_op!
— MethodJ = 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.
NLPModels.jac_op
— MethodJ = 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
.
NLPModels.jac_op_residual!
— MethodJx = 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.
NLPModels.jac_op_residual!
— MethodJx = 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.
NLPModels.jac_op_residual
— MethodJx = jac_op_residual(nls, x)
Computes $J(x)$, the Jacobian of the residual at x, in linear operator form.
NLPModels.jac_residual
— MethodJx = jac_residual(nls, x)
Computes $J(x)$, the Jacobian of the residual at x.
NLPModels.jac_structure!
— Methodjac_structure!(nlp, rows, cols)
Return the structure of the constraints Jacobian in sparse coordinate format in place.
NLPModels.jac_structure
— Method(rows,cols) = jac_structure(nlp)
Return the structure of the constraints Jacobian in sparse coordinate format.
NLPModels.jac_structure_residual!
— Function(rows,cols) = jac_structure_residual!(nls, rows, cols)
Returns the structure of the constraint's Jacobian in sparse coordinate format in place.
NLPModels.jac_structure_residual
— Method(rows,cols) = jac_structure_residual(nls)
Returns the structure of the constraint's Jacobian in sparse coordinate format.
NLPModels.jprod!
— MethodJv = jprod!(nlp, x, v, Jv)
Evaluate $J(x)v$, the Jacobian-vector product at x
in place.
NLPModels.jprod!
— MethodJv = 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.
NLPModels.jprod
— MethodJv = jprod(nlp, x, v)
Evaluate $J(x)v$, the Jacobian-vector product at x
.
NLPModels.jprod_lin!
— FunctionJv = jprod_lin!(nlp, x, v, Jv)
Evaluate $J(x)v$, the linear Jacobian-vector product at x
in place.
NLPModels.jprod_lin!
— MethodJv = 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.
NLPModels.jprod_lin
— MethodJv = jprod_lin(nlp, x, v)
Evaluate $J(x)v$, the linear Jacobian-vector product at x
.
NLPModels.jprod_nln!
— FunctionJv = jprod_nln!(nlp, x, v, Jv)
Evaluate $J(x)v$, the nonlinear Jacobian-vector product at x
in place.
NLPModels.jprod_nln!
— MethodJv = 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.
NLPModels.jprod_nln
— MethodJv = jprod_nln(nlp, x, v)
Evaluate $J(x)v$, the nonlinear Jacobian-vector product at x
.
NLPModels.jprod_residual!
— FunctionJv = jprod_residual!(nls, x, v, Jv)
Computes the product of the Jacobian of the residual at x and a vector, i.e., $J(x)v$, storing it in Jv
.
NLPModels.jprod_residual!
— MethodJv = 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
.
NLPModels.jprod_residual
— MethodJv = jprod_residual(nls, x, v)
Computes the product of the Jacobian of the residual at x and a vector, i.e., $J(x)v$.
NLPModels.jth_hess
— MethodHx = 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.
NLPModels.jth_hess_coord!
— Functionvals = jth_hess_coord!(nlp, x, j, vals)
Evaluate the Hessian of j-th constraint at x
in sparse coordinate format, with vals
of length nlp.meta.nnzh
, in place. Only the lower triangle is returned.
NLPModels.jth_hess_coord
— Methodvals = 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.
NLPModels.jth_hess_residual
— MethodHj = jth_hess_residual(nls, x, j)
Computes the Hessian of the j-th residual at x.
NLPModels.jth_hprod!
— FunctionHv = jth_hprod!(nlp, x, v, j, Hv)
Evaluate the product of the Hessian of j-th constraint at x
with the vector v
in place.
NLPModels.jth_hprod
— MethodHv = jth_hprod(nlp, x, v, j)
Evaluate the product of the Hessian of j-th constraint at x
with the vector v
.
NLPModels.jtprod!
— MethodJtv = 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.
NLPModels.jtprod!
— MethodJtv = 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.
NLPModels.jtprod
— MethodJtv = jtprod(nlp, x, v)
Evaluate $J(x)^Tv$, the transposed-Jacobian-vector product at x
.
NLPModels.jtprod_lin!
— FunctionJtv = jtprod_lin!(nlp, x, v, Jtv)
Evaluate $J(x)^Tv$, the linear transposed-Jacobian-vector product at x
in place.
NLPModels.jtprod_lin!
— MethodJtv = 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.
NLPModels.jtprod_lin
— MethodJtv = jtprod_lin(nlp, x, v)
Evaluate $J(x)^Tv$, the linear transposed-Jacobian-vector product at x
.
NLPModels.jtprod_nln!
— FunctionJtv = jtprod_nln!(nlp, x, v, Jtv)
Evaluate $J(x)^Tv$, the nonlinear transposed-Jacobian-vector product at x
in place.
NLPModels.jtprod_nln!
— MethodJtv = 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.
NLPModels.jtprod_nln
— MethodJtv = jtprod_nln(nlp, x, v)
Evaluate $J(x)^Tv$, the nonlinear transposed-Jacobian-vector product at x
.
NLPModels.jtprod_residual!
— FunctionJtv = jtprod_residual!(nls, x, v, Jtv)
Computes the product of the transpose of the Jacobian of the residual at x and a vector, i.e., $J(x)^Tv$, storing it in Jtv
.
NLPModels.jtprod_residual!
— MethodJtv = 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
.
NLPModels.jtprod_residual
— MethodJtv = jtprod_residual(nls, x, v)
Computes the product of the transpose of the Jacobian of the residual at x and a vector, i.e., $J(x)^Tv$.
NLPModels.linearly_constrained
— Methodlinearly_constrained(nlp)
linearly_constrained(meta)
Returns whether the problem's constraints are known to be all linear.
NLPModels.lines_of_description
— Methodlines_of_description(meta)
Describe meta
for the show
function.
NLPModels.lines_of_description
— Methodlines_of_description(nls_meta)
Describe nls_meta
for the show
function.
NLPModels.lines_of_hist
— Methodlines_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
.
NLPModels.neval_cons
— Methodneval_cons(nlp)
Get the number of cons
evaluations.
NLPModels.neval_cons_lin
— Methodneval_cons_lin(nlp)
Get the number of cons
evaluations.
NLPModels.neval_cons_nln
— Methodneval_cons_nln(nlp)
Get the number of cons
evaluations.
NLPModels.neval_grad
— Methodneval_grad(nlp)
Get the number of grad
evaluations.
NLPModels.neval_hess
— Methodneval_hess(nlp)
Get the number of hess
evaluations.
NLPModels.neval_hess_residual
— Methodnevalhessresidual(nlp)
Get the number of hess
evaluations.
NLPModels.neval_hprod
— Methodneval_hprod(nlp)
Get the number of hprod
evaluations.
NLPModels.neval_hprod_residual
— Methodnevalhprodresidual(nlp)
Get the number of hprod
evaluations.
NLPModels.neval_jac
— Methodneval_jac(nlp)
Get the number of jac
evaluations.
NLPModels.neval_jac_lin
— Methodneval_jac_lin(nlp)
Get the number of jac
evaluations.
NLPModels.neval_jac_nln
— Methodneval_jac_nln(nlp)
Get the number of jac
evaluations.
NLPModels.neval_jac_residual
— Methodnevaljacresidual(nlp)
Get the number of jac
evaluations.
NLPModels.neval_jcon
— Methodneval_jcon(nlp)
Get the number of jcon
evaluations.
NLPModels.neval_jgrad
— Methodneval_jgrad(nlp)
Get the number of jgrad
evaluations.
NLPModels.neval_jhess
— Methodneval_jhess(nlp)
Get the number of jhess
evaluations.
NLPModels.neval_jhess_residual
— Methodnevaljhessresidual(nlp)
Get the number of jhess
evaluations.
NLPModels.neval_jhprod
— Methodneval_jhprod(nlp)
Get the number of jhprod
evaluations.
NLPModels.neval_jprod
— Methodneval_jprod(nlp)
Get the number of jprod
evaluations.
NLPModels.neval_jprod_lin
— Methodneval_jprod_lin(nlp)
Get the number of jprod
evaluations.
NLPModels.neval_jprod_nln
— Methodneval_jprod_nln(nlp)
Get the number of jprod
evaluations.
NLPModels.neval_jprod_residual
— Methodnevaljprodresidual(nlp)
Get the number of jprod
evaluations.
NLPModels.neval_jtprod
— Methodneval_jtprod(nlp)
Get the number of jtprod
evaluations.
NLPModels.neval_jtprod_lin
— Methodneval_jtprod_lin(nlp)
Get the number of jtprod
evaluations.
NLPModels.neval_jtprod_nln
— Methodneval_jtprod_nln(nlp)
Get the number of jtprod
evaluations.
NLPModels.neval_jtprod_residual
— Methodnevaljtprodresidual(nlp)
Get the number of jtprod
evaluations.
NLPModels.neval_obj
— Methodneval_obj(nlp)
Get the number of obj
evaluations.
NLPModels.neval_residual
— Methodneval_residual(nlp)
Get the number of residual
evaluations.
NLPModels.nls_meta
— Methodnls_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
.
NLPModels.obj
— Functionf = obj(nlp, x)
Evaluate $f(x)$, the objective function of nlp
at x
.
NLPModels.obj
— Methodf = obj(nls, x)
f = obj(nls, x, Fx; recompute::Bool=true)
Evaluate f(x)
, the objective function of nls::AbstractNLSModel
. Fx
is overwritten with the value of the residual F(x)
. If recompute
is true
, then Fx
is updated with the residual at x
.
NLPModels.objcons!
— Methodf, c = objcons!(nlp, x, c)
Evaluate $f(x)$ and $c(x)$ at x
. c
is overwritten with the value of $c(x)$.
NLPModels.objcons
— Methodf, c = objcons(nlp, x)
Evaluate $f(x)$ and $c(x)$ at x
.
NLPModels.objgrad!
— Methodf, g = objgrad!(nlp, x, g)
Evaluate $f(x)$ and $∇f(x)$ at x
. g
is overwritten with the value of $∇f(x)$.
NLPModels.objgrad!
— Methodf, g = objgrad!(nls, x, g)
f, g = objgrad!(nls, x, g, Fx; recompute::Bool=true)
Evaluate f(x) and ∇f(x) of nls::AbstractNLSModel
at x
. Fx
is overwritten with the value of the residual F(x)
. If recompute
is true
, then Fx
is updated with the residual at x
.
NLPModels.objgrad
— Methodf, g = objgrad(nlp, x)
Evaluate $f(x)$ and $∇f(x)$ at x
.
NLPModels.reset_data!
— Methodreset_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.
NLPModels.residual!
— FunctionFx = residual!(nls, x, Fx)
Computes $F(x)$, the residual at x.
NLPModels.residual
— MethodFx = residual(nls, x)
Computes $F(x)$, the residual at x.
NLPModels.show_counters
— Methodshow_counters(io, counters, fields)
Show the fields
of the struct counters
.
NLPModels.show_header
— Methodshow_header(io, nlp)
Show a header for the specific nlp
type. Should be imported and defined for every model implementing the NLPModels API.
NLPModels.sparsityline
— Methodsparsityline(s, v, maxv)
Return a string of the form
______NAME______: ( 80.00% sparsity) 5
where:
______NAME______
iss
with padding to the left and length 16.- The sparsity value is given by
v / maxv
. - The number
5
is v.
NLPModels.sum_counters
— Methodsum_counters(nlp)
Sum all counters of problem nlp
except cons
, jac
, jprod
and jtprod
.
NLPModels.sum_counters
— Methodsum_counters(counters)
Sum all counters of counters
except cons
, jac
, jprod
and jtprod
.
NLPModels.unconstrained
— Methodunconstrained(nlp)
unconstrained(meta)
Returns whether the problem in unconstrained.
NLPModels.@default_counters
— Macro@default_counters Model inner
Define functions relating counters of Model
to counters of Model.inner
.
NLPModels.@default_nlscounters
— Macro@default_nlscounters Model inner
Define functions relating NLS counters of Model
to NLS counters of Model.inner
.
NLPModels.@lencheck
— Macro@lencheck n x y z …
Check that arrays x
, y
, z
, etc. have a prescribed length n
.
NLPModels.@rangecheck
— Macro@rangecheck ℓ u i j k …
Check that values i
, j
, k
, etc. are in the range [ℓ,u]
.