# Reference

## Contents

## Index

`AmplNLReader.AmplNLPMeta`

— Type`AmplNLPMeta <: 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.

`AmplNLPMeta(nvar; kwargs...)`

Create an `AmplNLPMeta`

with `nvar`

variables. The following keyword arguments are accepted:

`x0`

: initial guess`lvar`

: vector of lower bounds`uvar`

: vector of upper bounds`nbv`

: number of linear binary variables`niv`

: number of linear non-binary integer variables`nlvb`

: number of nonlinear variables in both objectives and constraints`nlvo`

: number of nonlinear variables in objectives (includes nlvb)`nlvc`

: number of nonlinear variables in constraints (includes nlvb)`nlvbi`

: number of integer nonlinear variables in both objectives and constraints`nlvci`

: number of integer nonlinear variables in constraints only`nlvoi`

: number of integer nonlinear variables in objectives only`nwv`

: number of linear network (arc) variables`ncon`

: number of general constraints`y0`

: initial Lagrange multipliers`lcon`

: vector of constraint lower bounds`ucon`

: vector of constraint upper bounds`nnzo`

: number of nonzeros in all objectives gradients`nnzj`

: number of elements needed to store the nonzeros in the sparse Jacobian`nnzh`

: number of elements needed to store the nonzeros in the sparse Hessian`nlin`

: number of linear constraints`nnln`

: number of nonlinear general constraints`nnnet`

: number of nonlinear network constraints`nlnet`

: number of linear network constraints`lin`

: indices of linear constraints`nln`

: indices of nonlinear constraints`nnet`

: indices of nonlinear network constraints`lnet`

: indices of linear network constraints`minimize`

: true if optimize == minimize`nlo`

: number of nonlinear objectives`islp`

: true if the problem is a linear program`name`

: problem name