MPS is both an executable binary that can be run, and a library that can be used in Rust programs.

Installing the command-line executable

Assuming you have Rust/Cargo installed, run this command in a terminal:

cargo install mps

It will make the mps command available in your PATH if you've allowed the PATH to be modified when installing Rust. cargo uninstall mps uninstalls.

Adding `mps` library as a dependency

Run this command in a terminal, in your project's directory:

cargo add mps

To add it manually, edit your project's Cargo.toml file and add to the [dependencies] section:

mps = "1.0.0"

The mps library will be automatically available globally.

Back to the crate overview.

Readme

mps

A fast MPS parser written in Rust

About

mps is a parser for the Mathematical Programming System (MPS) file format, commonly used to represent optimization problems.

This crate provides both a library and a CLI for parsing MPS data. Key features include:

Configurable Parsing:
- Supported feature flags:
  - trace - Enhanced debugging and statistics via nom_tracable and nom_locate.
  - proptest - Property testing integrations.
  - cli - Command line interface.
Robustness: Extensively tested against Netlib LP test suite.
Performance: Benchmarked using Criterion.rs.

Examples

Library

use mps::Parser;

let contents = "MPS data...";
match Parser::<f32>::parse(&contents) {
    Ok((_, model)) => { /* use MPS model */ },
    Err(e) => eprintln!("Parsing error: {}", e),
}

CLI

$ mps --input-path ./data/netlib/afiro

Usage as a flake

Add mps to your flake.nix:

{
  inputs.mps.url = "https://flakehub.com/f/integrated-reasoning/mps/*.tar.gz";

  outputs = { self, mps }: {
    # Use in your outputs
  };
}

Running with Docker

docker run -it integratedreasoning/mps:latest

Roadmap

Semantic validation

Conduct semantic validation to uncover potential issues upfront before passing models to the solver.

Structural checks
- Detect and flag unreachable constraints that can be automatically satisfied or violated given bounds
- Identify redundant constraints that are logical duplicates of existing rows
- Check for dominating constraints that make other constraints redundant
- Validate presence of slack variables where needed to avoid unboundedness
- Verify dual feasibility through analyzing bounds, objective, and right hand sides
- Cross-validate variable objective costs with cost coefficients from other provided perspectives
- Check for overlapping/duplicated terms across constraints modeling the same logical relationship
- Identify variables not participating in any constraints, unless intended as free variables
Numerical checks
- Check for numerics - flag near zero coefficients that could lead to weak formulations
- Raise issues with over-aggressive bounds that over-constrain the feasible region undesirably
- Analyze term sparsity to call out potential compact formulation opportunities
- Diagnose poor model scaling that could introduce solution inaccuracies
Logical checks
- Flag discrete variables implicitly constrained to be continuous due to coefficient assignments
- Check for logically contradictory bounds on variables that conflict with constraint implications

Installing the command-line executable

Adding mps library as a dependency

Adding `mps` library as a dependency