# AmplNLReader.jl: A Julia interface to AMPL

### Stable release

- Documentation:

### Development version

## How to Install

At the Julia prompt, clone this repository and build:

```
julia> Pkg.add("AmplNLReader")
julia> Pkg.build("AmplNLReader")
```

## Testing

```
julia> Pkg.test("AmplNLReader")
```

## Creating a Model

For an introduction to the AMPL modeling language, see

- R. Fourer, D. M. Gay, and B. W. Kernighan, AMPL: A Mathematical Programming Language, Management Science 36, pp. 519-554, 1990.
- R. Fourer, D. M. Gay, and B. W. Kernighan, AMPL: A Modeling Language for Mathematical Programming, Duxbury Press / Brooks/Cole Publishing Company, 2003.
- D. Orban, The Lightning AMPL Tutorial. A Guide for Nonlinear Optimization Users, GERAD Technical Report G-2009-66, 2009.

Suppose you have an AMPL model represented by the model and data files `mymodel.mod`

and `mymodel.dat`

. Decode this model as a so-called `nl`

file using

```
ampl -ogmymodel mymodel.mod mymodel.dat
```

For example:

```
julia> using AmplNLReader
julia> hs33 = AmplModel("hs033.nl")
Minimization problem hs033.nl
nvar = 3, ncon = 2 (0 linear)
julia> print(hs33)
Minimization problem hs033.nl
nvar = 3, ncon = 2 (0 linear)
lvar = 1x3 Array{Float64,2}:
0.0 0.0 0.0
uvar = 1x3 Array{Float64,2}:
Inf Inf 5.0
lcon = 1x2 Array{Float64,2}:
-Inf 4.0
ucon = 1x2 Array{Float64,2}:
0.0 Inf
x0 = 1x3 Array{Float64,2}:
0.0 0.0 3.0
y0 = 1x2 Array{Float64,2}:
-0.0 -0.0
```

There is support for holding multiple models in memory simultaneously. This should be transparent to the user.

## Optimization Problems

`AmplNLReader.jl`

currently focuses on continuous problems conforming to `NLPModels.jl`

.

`AmplModel`

objects support all methods associated to `NLPModel`

objects.
Please see the `NLPModels.jl`

documentation for more information.
The following table lists extra methods associated to an `AmplModel`

.
See Hooking your Solver to AMPL for background.

Method |
Notes |

`write_sol(nlp, msg, x, y)` |
Write primal and dual solutions to file |

## Missing Methods

- methods for LPs (sparse cost, sparse constraint matrix)
- methods to check optimality conditions.

This content is released under the MIT License.