# GLPK.jl

**Repository Path**: hydro3/GLPK.jl

## Basic Information

- **Project Name**: GLPK.jl
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-10-01
- **Last Updated**: 2024-08-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

#  GLPK.jl

| **Build Status**                                                                                    |
|:---------------------------------------------------------------------------------------------------:|
| [![][travis-img]][travis-url] [![][appveyor-img]][appveyor-url] [![][coveralls-img]][coveralls-url] |


GLPK.jl is a wrapper for the [GNU Linear Programming Kit library](http://www.gnu.org/software/glpk).
It makes it possible to access nearly all of GLPK functionality from within Julia programs.

This package is part of [the JuliaOpt project](http://www.juliaopt.org/).

## Installation

The package is registered in the [General registry](https://github.com/JuliaRegistries/General/) and so can be installed with `Pkg.add`.

```julia
julia> import Pkg

julia> Pkg.add("GLPK")
```

GLPK.jl will use [BinaryProvider.jl](https://github.com/JuliaPackaging/BinaryProvider.jl) to automatically install the GLPK binaries with [GMP](https://gmplib.org) support.

## Custom Installation

To install custom built GLPK binaries set the environmental variable `JULIA_GLPK_LIBRARY_PATH` and call `import Pkg; Pkg.build("GLPK")`. For instance, if the libraries are installed in `/opt/lib` just call
```julia
ENV["JULIA_GLPK_LIBRARY_PATH"]="/opt/lib"
Pkg.build("GLPK")
```

If you do not want BinaryProvider to download the default binaries on install set  `JULIA_GLPK_LIBRARY_PATH`  before calling `import Pkg; Pkg.add("GLPK")`.

To switch back to the default binaries clear `JULIA_GLPK_LIBRARY_PATH` and call `import Pkg; Pkg.build("GLPK")`.

## `GLPK.Optimizer`

Use `GLPK.Optimizer` to create a new optimizer object:

In the following examples the time limit is set to one minute and logging is turned off.
```julia
using GLPK
model = GLPK.Optimizer(tm_lim = 60000, msg_lev = GLPK.OFF)
```
For JuMP, use:
```julia
using JuMP, GLPK
model = Model(
    with_optimizer(GLPK.Optimizer, tm_lim = 60000, msg_lev = GLPK.OFF)
)
```

**Note: previous versions of `GLPK.jl` required you to choose either `GLPKSolverLP` or `GLPKSolverMIP`. This is no longer needed; just use `GLPK.Optimizer`.**

## Pre-emptive checks

`GLPK.jl` has a lot of pre-emptive checks to catch cases where the C API might
throw an uninformative error. However, in benchmarks, this takes a
non-negligible amount of time (e.g. 20% in add_constraints). At the risk of
possibly running into an uninformative error, you can run the following after
importing GLPK to disable these checks:
```julia
using GLPK
GLPK.jl_set_preemptive_check(false)
```

[travis-img]: https://api.travis-ci.org/JuliaOpt/GLPK.jl.svg?branch=master
[travis-url]: https://travis-ci.org/JuliaOpt/GLPK.jl

[appveyor-img]: https://ci.appveyor.com/api/projects/status/4t5e2dir3gp7fb6h?svg=true
[appveyor-url]: https://ci.appveyor.com/project/JuliaOpt/glpk-jl

[coveralls-img]: https://img.shields.io/coveralls/JuliaOpt/GLPK.jl.svg
[coveralls-url]: https://coveralls.io/r/JuliaOpt/GLPK.jl