https://github.com/ztlpn/minilp

A pure Rust linear programming solver
https://github.com/ztlpn/minilp

linear-programming rust simplex-algorithm

Last synced: about 1 month ago
JSON representation

A pure Rust linear programming solver

• Host: GitHub
• URL: https://github.com/ztlpn/minilp
• Owner: ztlpn
• License: apache-2.0
• Archived: true
• Created: 2020-01-24T14:46:30.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-11-11T03:17:06.000Z (over 3 years ago)
• Last Synced: 2024-01-27T13:38:59.137Z (5 months ago)
• Topics: linear-programming, rust, simplex-algorithm
• Language: Rust
• Homepage:
• Size: 298 KB
• Stars: 73
• Watchers: 4
• Forks: 9
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-rust-formalized-reasoning - minilp - linear programming solver. (Projects / Provers and Solvers)

README

        
# minilp

[![Crates.io](https://img.shields.io/crates/v/minilp.svg)](https://crates.io/crates/minilp)

[![Documentation](https://docs.rs/minilp/badge.svg)](https://docs.rs/minilp/)

A fast linear programming solver library.

[Linear programming](https://en.wikipedia.org/wiki/Linear_programming) is a technique for

finding the minimum (or maximum) of a linear function of a set of continuous variables

subject to linear equality and inequality constraints.

## Features

* Pure Rust implementation.

* Able to solve problems with hundreds of thousands of variables and constraints.

* Incremental: add constraints to an existing solution without solving it from scratch.

* Problems can be defined via an API or parsed from an

  [MPS](https://en.wikipedia.org/wiki/MPS_(format)) file.

Warning: this is an early-stage project. Although the library is already quite powerful and fast,

it will probably cycle, lose precision or panic on some harder problems. Please report

bugs and contribute code!

## Examples

Basic usage

```rust

use minilp::{Problem, OptimizationDirection, ComparisonOp};

// Maximize an objective function x + 2 * y of two variables x >= 0 and 0 <= y <= 3

let mut problem = Problem::new(OptimizationDirection::Maximize);

let x = problem.add_var(1.0, (0.0, f64::INFINITY));

let y = problem.add_var(2.0, (0.0, 3.0));

// subject to constraints: x + y <= 4 and 2 * x + y >= 2.

problem.add_constraint(&[(x, 1.0), (y, 1.0)], ComparisonOp::Le, 4.0);

problem.add_constraint(&[(x, 2.0), (y, 1.0)], ComparisonOp::Ge, 2.0);

// Optimal value is 7, achieved at x = 1 and y = 3.

let solution = problem.solve().unwrap();

assert_eq!(solution.objective(), 7.0);

assert_eq!(solution[x], 1.0);

assert_eq!(solution[y], 3.0);

```

For a more involved example, see [examples/tsp](examples#tsp), a solver for the travelling

salesman problem.

## License

This project is licensed under the [Apache License, Version 2.0](./LICENSE).