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