Reference

Contents

Index

NLPModels.cons!Method

Evaluates the constraints $c(x)$ excluding any which appear in $G(x)$ or $H(x)$, in place.

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NLPModels.consMethod

Evaluates the constraints $c(x)$ excluding any which appear in $G(x)$ or $H(x)$, to a newly allocated vector.

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NLPModels.gradMethod

Evaluates the gradient of the MPCC objective $\nabla f(x)$ to a newly allocated vector.

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NLPModels.hessMethod

Evaluates the Hessian of the Lagrangian, without the complementarity contribution.

Assumes zero multipliers.

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NLPModels.hess_coord!Method

Evaluates the COO structure of the Hessian of the Lagrangian, without the complementarity contribution, in place.

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NLPModels.hess_coord!Method

Evaluates the COO structure of the Hessian of the Lagrangian, without the complementarity contribution, in place.

Assumes zero multipliers.

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NLPModels.hess_coordMethod

Evaluates the COO values of the Hessian of the Lagrangian, without the complementarity contribution, to newly allocated vectors.

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NLPModels.hess_coordMethod

Evaluates the COO values of the Hessian of the Lagrangian, without the complementarity contribution, to newly allocated vectors.

Assumes zero multipliers.

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NLPModels.hess_structure!Method

Evaluates the COO structure of the Hessian of the Lagrangian, without the complementarity contribution, in place.

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NLPModels.hess_structureMethod

Evaluates the COO structure of the Hessian of the Lagrangian, without the complementarity contribution, to newly allocated vectors.

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NLPModels.jacMethod

Evaluates the constraint jacobian $\nabla c(x)$ excluding any constraints which appear in $G(x)$ or $H(x)$.

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NLPModels.jac_coord!Method

Evaluates the COO values of jacobian $\nabla c(x)$ excluding any constraints which appear in $G(x)$ or $H(x)$, in place.

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NLPModels.jac_coordMethod

Evaluates the COO values of jacobian $\nabla c(x)$ excluding any constraints which appear in $G(x)$ or $H(x)$, to a newly allocated vector.

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NLPModels.jac_structure!Method

Evaluates the COO structure of jacobian $\nabla c(x)$ excluding any constraints which appear in $G(x)$ or $H(x)$, in place.

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NLPModels.jac_structureMethod

Evaluates the COO structure of jacobian $\nabla c(x)$ excluding any constraints which appear in $G(x)$ or $H(x)$, to newly allocated vectors.

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NLPModels.objgrad!Method

Evaluates in place the gradient of the MPCC objective $\nabla f(x)$ and returns the objective $f(x)$.

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MPCCModels.MPCCModelType

Concrete type for problems in the form:

min f(x)
s.t. lbc ≤ c(x) ≤ ubc
     lbG ≤ G(x) ⟂ H(x) ≥ lbH

where G(x) and H(x) are defined by index sets into the x and c(x) of an nlp of the from:

min f(x)
s.t. lbc ≤ c(x) ≤ ubc
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MPCCModels.MPCCModelMethod

Constructor for MPCCModel in the form:

 min f(x)
 s.t. lbc ≤ c(w) ≤ ubc
      lbx₁ ≤ x₁ ⟂x₂ ≥ lbx₂

where x₁ and x₂ are defined by index sets indvcc1 and indvcc2.

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