Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/integrated-reasoning/mps
A fast MPS parser written in Rust
https://github.com/integrated-reasoning/mps
Last synced: 6 days ago
JSON representation
A fast MPS parser written in Rust
- Host: GitHub
- URL: https://github.com/integrated-reasoning/mps
- Owner: integrated-reasoning
- License: mit
- Created: 2023-12-15T01:35:33.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-04-01T07:22:36.000Z (8 months ago)
- Last Synced: 2024-05-22T20:34:27.594Z (6 months ago)
- Language: Rust
- Size: 26.5 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-rust-formalized-reasoning - mps - fast MPS parser. (Projects / Libraries)
README
# mps
A fast MPS parser written in Rust
[](https://github.com/integrated-reasoning/mps/actions/workflows/ci.yml)
[](https://deps.rs/repo/github/integrated-reasoning/mps)
[](LICENSE-MIT)
[](https://codecov.io/github/integrated-reasoning/mps)
[](https://flakehub.com/flake/integrated-reasoning/mps)
[](https://blog.rust-lang.org/2023/08/03/Rust-1.71.1.html)
## 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](http://www.netlib.org/lp/data/).
- **Performance**: Benchmarked using [Criterion.rs](https://github.com/bheisler/criterion.rs).
## Examples
**Library**
```rust
use mps::Parser;
let contents = "MPS data...";
match Parser::::parse(&contents) {
Ok((_, model)) => { /* use MPS model */ },
Err(e) => eprintln!("Parsing error: {}", e),
}
```
**CLI**
```bash
$ mps --input-path ./data/netlib/afiro
```
## Usage as a flake
Add `mps` to your `flake.nix`:
```nix
{
inputs.mps.url = "https://flakehub.com/f/integrated-reasoning/mps/*.tar.gz";
outputs = { self, mps }: {
# Use in your outputs
};
}
```
## Running with Docker
```bash
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