A parser for LP files,and the four tools built on it.

What's here

Four ways to use it

The Rust library

Library capabilities

• ParsingFull LP format coverage: multiple objectives, all constraint senses, comments, and section keywords across the CPLEX, Gurobi, Xpress, and Mosek dialects.
• Variable typesInteger, general, bounded, free, and semi-continuous variables.
• WritingRound-trip safe LP output with configurable problem-name comment, line length, decimal precision, and section spacing.
• ModificationRename, update, or remove objectives, constraints, variables, coefficients, and RHS values in place.
• AnalysisStatistics, matrix density and sparsity, coefficient ranges, and issue detection with configurable numeric thresholds.
• DiffStructural comparison of two problems (diff feature) with numeric tolerances and regex name rewriting.
• SerialisationJSON and YAML in and out (serde feature).
• SolvingHand problems to CBC, GLPK, Gurobi, or CPLEX via the lp-solvers feature.

Parse and inspect

use lp_parser_rs::{parser::parse_file, problem::LpProblem};

let content = parse_file(Path::new("problem.lp"))?;
let problem = LpProblem::parse(&content)?;
println!("{} objectives, {} constraints, {} variables",
    problem.objective_count(), problem.constraint_count(), problem.variable_count());

Modify and write back

let mut problem = LpProblem::parse(&std::fs::read_to_string("problem.lp")?)?;

problem.update_objective_coefficient("profit", "x1", 5.0)?;
problem.rename_objective("profit", "total_profit")?;
problem.update_constraint_coefficient("capacity", "x1", 2.0)?;
problem.update_constraint_rhs("capacity", 200.0)?;
problem.rename_variable("x1", "production_a")?;
problem.update_variable_type("production_a", VariableType::Integer)?;

std::fs::write("modified.lp", write_lp_string(&problem)?)?;

The full modification surface on LpProblem: update_objective_coefficient, rename_objective, remove_objective, update_constraint_coefficient, update_constraint_rhs, rename_constraint, remove_constraint, rename_variable, update_variable_type, remove_variable. Writer knobs live on LpWriterOptions: include_problem_name, max_line_length, decimal_precision, include_section_spacing.

Cargo features

The command line

CLI: lp_parser

Every subcommand writes to stdout or a file (-o) as text, JSON, or YAML (-f, plus --pretty). Global flags: -v/--verbose (repeatable), -q/--quiet.

parse — display file structure

lp_parser parse problem.lp
lp_parser parse problem.lp --format yaml -o problem.yaml

info — summary statistics

Counts plus optional full listings via --variables, --constraints, --objectives.

lp_parser info problem.lp --variables --constraints --objectives
lp_parser info problem.lp --format json --pretty

analyze — structural analysis & issue detection

lp_parser analyze problem.lp --issues-only
lp_parser analyze problem.lp --large-coeff-threshold 1e8 --ratio-threshold 1e5

# example output
summary: { name: diet, sense: Minimize, objective_count: 1, constraint_count: 7, variable_count: 16, density: 0.571 }
variables: { type_distribution: { upper_bounded: 9, double_bounded: 7 }, discrete_variable_count: 0 }
constraints: { type_distribution: { equality: 7 }, rhs_range: { min: 30.0, max: 50000.0 } }
coefficients: { constraint_coeff_range: { min: 0.1, max: 3055.2 }, coefficient_ratio: 101840.0 }
issues: []

diff — compare two LP files

lp_parser diff old.lp new.lp --abs-tol 1e-6 --rel-tol 1e-9
lp_parser diff old.lp new.lp --rename '\[\d+\]$' '[N]' --format json --pretty

convert — translate to another format

Targets: lp, csv, json, yaml. LP output honours --precision, --max-line-length, --no-problem-name, and --compact; CSV writes constraints.csv, objectives.csv, and variables.csv to a directory.

lp_parser convert problem.lp --format lp --precision 4 --compact
lp_parser convert problem.lp --format csv --output ./out

solve — run an external solver

Solves with CBC (default) or GLPK from your PATH (-s/--solver). Multiple objectives and strict inequalities error; SOS constraints are ignored with a warning.

lp_parser solve problem.lp --solver glpk --format json --pretty

The terminal UI

TUI: lp_diff

An interactive ratatui explorer and diff viewer for LP and MPS files. One file opens a single-model explorer; two files diff them, in any mix of formats (lp_diff model.lp model.mps). It has grown well past a diff viewer: there is a background solver, in-memory what-if edits, solution-preserving rewrites, and a diagnostics pane that names the constraints slowing a solve down.

• Explore and diffFive sections (Summary, Variables, Constraints, Objectives, Numerics), coefficient-level side-by-side diffs, raw LP text view, kind filters, delta sorts, and tolerances you can cycle live.
• SearchTelescope-style pop-up across every section: fuzzy by default, with r: regex, s: substring, and c: content modes. Plus a command palette and a 100-entry jumplist.
• SolveS solves either file, or both in parallel, with HiGHS on a background thread. Results tab into Summary, Variables, Constraints, Log and Duals, with solution diffing, ranked duals, CSV export, and e for infeasibility diagnosis.
• What-ifE edits a constraint's RHS in memory and re-solves it against the baseline, so you can test how far a shadow price actually holds.
• RewriteP applies solution-preserving presolve rules (singleton rows to bounds, bound propagation, integer rounding, redundant and forcing rows, empty rows and columns) to a fixpoint, then compares the original and rewritten solves.
• DiagnosticsD joins solver telemetry to the model, giving every row and column its coefficient spread alongside its shadow price, and ranking the worst-conditioned, most degenerate and densest by name.

Python bindings

Python: parse_lp

The same Rust engine behind a typed Python API: parsing, data access, modification, LP writing, analysis with configurable thresholds, CSV export, and problem-to-problem comparison.

from parse_lp import LpParser

parser = LpParser("problem.lp")
parser.parse()

print(parser.name, parser.sense, parser.variable_count())

parser.update_objective_coefficient("OBJ", "x1", 5.0)
parser.rename_variable("x2", "production")
parser.update_constraint_rhs("C1", 100.0)

parser.save_to_file("modified_problem.lp")

analysis = parser.analyze()
print(analysis["summary"]["density"])

Getting it

Install

Develop with cargo insta test --all-features and review snapshot changes with cargo insta review. Contributions welcome: open a pull request.