ParametricNLPModelMeta(; kwargs...)

Metadata related to parameters including number of parameters, nonzero counts, and API availability.

get_param_values(nlp)

Return $p$, the current parameter values stored by nlp.

grad_param!(nlp, x, g)

Evaluate $∇ₚf(x, p)$, the gradient of the objective function with respect to the parameters, at x in place.

hess_param_coord!(nlp, x, y, vals; obj_weight = 1)

Evaluate $∇ₓₚL(x, y, p)$, the mixed block of the Lagrangian Hessian, at (x, y) in sparse coordinate format in place.

hess_param_structure!(nlp, rows, cols)

Return the structure of $∇ₓₚL(x, y, p)$, the mixed block of the Lagrangian Hessian, in sparse coordinate format in place.

hpprod!(nlp, x, y, v, Hv; obj_weight = 1)

Evaluate $∇ₓₚL(x, y, p)v$, the mixed-Hessian-vector product, at (x, y) in place.

hptprod!(nlp, x, y, v, Htv; obj_weight = 1)

Evaluate $∇ₓₚL(x, y, p)ᵀv$, the transposed-mixed-Hessian-vector product, at (x, y) in place.

jac_param_coord!(nlp, x, vals)

Evaluate $Jₚ(x, p)$, the constraints Jacobian with respect to the parameters, at x in sparse coordinate format in place.

jac_param_structure!(nlp, rows, cols)

Return the structure of $Jₚ(x, p)$, the constraints Jacobian with respect to the parameters, in sparse coordinate format in place.

jpprod!(nlp, x, v, Jv)

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

jptprod!(nlp, x, v, Jtv)

Evaluate $Jₚ(x, p)ᵀv$, the transposed-parameter-Jacobian-vector product, at x in place.

lcon_jac_param_coord!(nlp, vals)

Evaluate $∂ℓᶜ(p) / ∂p$ in sparse coordinate format in place.

lcon_jac_param_structure!(nlp, rows, cols)

Return the structure of $∂ℓᶜ(p) / ∂p$ in sparse coordinate format in place.

lcon_jpprod!(nlp, v, Jv)

Evaluate $(∂ℓᶜ(p) / ∂p) v$ in place.

lcon_jptprod!(nlp, v, Jtv)

Evaluate $(∂ℓᶜ(p) / ∂p)ᵀ v$ in place.

lvar_jac_param_coord!(nlp, vals)

Evaluate $∂ℓˣ(p) / ∂p$ in sparse coordinate format in place.

lvar_jac_param_structure!(nlp, rows, cols)

Return the structure of $∂ℓˣ(p) / ∂p$ in sparse coordinate format in place.

lvar_jpprod!(nlp, v, Jv)

Evaluate $(∂ℓˣ(p) / ∂p) v$ in place.

lvar_jptprod!(nlp, v, Jtv)

Evaluate $(∂ℓˣ(p) / ∂p)ᵀ v$ in place.

set_param_values!(nlp, p)

Overwrite the parameter values stored by nlp with $p$.

ucon_jac_param_coord!(nlp, vals)

Evaluate $∂uᶜ(p) / ∂p$ in sparse coordinate format in place.

ucon_jac_param_structure!(nlp, rows, cols)

Return the structure of $∂uᶜ(p) / ∂p$ in sparse coordinate format in place.

ucon_jpprod!(nlp, v, Jv)

Evaluate $(∂uᶜ(p) / ∂p) v$ in place.

ucon_jptprod!(nlp, v, Jtv)

Evaluate $(∂uᶜ(p) / ∂p)ᵀ v$ in place.

uvar_jac_param_coord!(nlp, vals)

Evaluate $∂uˣ(p) / ∂p$ in sparse coordinate format in place.

uvar_jac_param_structure!(nlp, rows, cols)

Return the structure of $∂uˣ(p) / ∂p$ in sparse coordinate format in place.

uvar_jpprod!(nlp, v, Jv)

Evaluate $(∂uˣ(p) / ∂p) v$ in place.

uvar_jptprod!(nlp, v, Jtv)

Evaluate $(∂uˣ(p) / ∂p)ᵀ v$ in place.

grad_param(nlp, x)

Evaluate $∇ₚf(x, p)$, the gradient of the objective function with respect to the parameters, at x.

hess_param_coord!(nlp, x, vals; obj_weight = 1)

Evaluate $∇ₓₚL(x, 0, p)$, the mixed block of the Lagrangian Hessian with zero constraint multipliers, in sparse coordinate format in place.

hess_param_coord(nlp, x, y; obj_weight = 1)

Evaluate $∇ₓₚL(x, y, p)$, the mixed block of the Lagrangian Hessian at (x, y), in sparse coordinate format.

hess_param_coord(nlp, x; obj_weight = 1)

Evaluate $∇ₓₚL(x, 0, p)$, the mixed block of the Lagrangian Hessian with zero constraint multipliers, in sparse coordinate format.

hess_param_structure(nlp)

Return the structure of $∇ₓₚL(x, y, p)$, the mixed block of the Lagrangian Hessian, in sparse coordinate format.

hpprod!(nlp, x, v, Hv; obj_weight = 1)

Evaluate $∇ₓₚL(x, 0, p)v$, the mixed-Hessian-vector product with zero constraint multipliers, in place.

hpprod(nlp, x, y, v; obj_weight = 1)

Evaluate $∇ₓₚL(x, y, p)v$, the mixed-Hessian-vector product at (x, y).

hpprod(nlp, x, v; obj_weight = 1)

Evaluate $∇ₓₚL(x, 0, p)v$, the mixed-Hessian-vector product with zero constraint multipliers.

hptprod(nlp, x, y, v; obj_weight = 1)

Evaluate $∇ₓₚL(x, y, p)ᵀv$, the transposed-mixed-Hessian-vector product at (x, y).

jac_param_coord(nlp, x)

Evaluate $Jₚ(x, p)$, the constraints Jacobian with respect to the parameters, at x in sparse coordinate format.

jac_param_structure(nlp)

Return the structure of $Jₚ(x, p)$, the constraints Jacobian with respect to the parameters, in sparse coordinate format.

jpprod(nlp, x, v)

Evaluate $Jₚ(x, p)v$, the parameter-Jacobian-vector product at x.

jptprod(nlp, x, v)

Evaluate $Jₚ(x, p)ᵀv$, the transposed-parameter-Jacobian-vector product at x.

lcon_jac_param_coord(nlp)

Evaluate $∂ℓᶜ(p) / ∂p$ in sparse coordinate format.

lcon_jac_param_structure(nlp)

Return the structure of $∂ℓᶜ(p) / ∂p$ in sparse coordinate format.

lcon_jpprod(nlp, v)

Evaluate $(∂ℓᶜ(p) / ∂p)v$.

lcon_jptprod(nlp, v)

Evaluate $(∂ℓᶜ(p) / ∂p)ᵀv$.

lvar_jac_param_coord(nlp)

Evaluate $∂ℓˣ(p) / ∂p$ in sparse coordinate format.

lvar_jac_param_structure(nlp)

Return the structure of $∂ℓˣ(p) / ∂p$ in sparse coordinate format.

lvar_jpprod(nlp, v)

Evaluate $(∂ℓˣ(p) / ∂p)v$.

lvar_jptprod(nlp, v)

Evaluate $(∂ℓˣ(p) / ∂p)ᵀv$.

ucon_jac_param_coord(nlp)

Evaluate $∂uᶜ(p) / ∂p$ in sparse coordinate format.

ucon_jac_param_structure(nlp)

Return the structure of $∂uᶜ(p) / ∂p$ in sparse coordinate format.

ucon_jpprod(nlp, v)

Evaluate $(∂uᶜ(p) / ∂p)v$.

ucon_jptprod(nlp, v)

Evaluate $(∂uᶜ(p) / ∂p)ᵀv$.

uvar_jac_param_coord(nlp)

Evaluate $∂uˣ(p) / ∂p$ in sparse coordinate format.

uvar_jac_param_structure(nlp)

Return the structure of $∂uˣ(p) / ∂p$ in sparse coordinate format.

uvar_jpprod(nlp, v)

Evaluate $(∂uˣ(p) / ∂p)v$.

uvar_jptprod(nlp, v)

Evaluate $(∂uˣ(p) / ∂p)ᵀv$.