LP (file format)

LP (file format)
Name	LP
Extension	.lp
Mime	text/x-lp
Owner	International LP Consortium
Released	1998
Genre	data interchange, optimization

LP (file format) is a plain-text data interchange format used to represent mathematical optimization problems, particularly linear programming models and related constructs. It serves as an interoperable medium between modeling systems, solvers, and academic tools in contexts involving operations research, decision science, and computational mathematics.

Overview

The LP format encodes objective functions, constraints, bounds, and variable declarations in a human-readable syntax that predates and complements modeling languages such as AMPL, GAMS, and MathProg. It is widely used in conjunction with solvers like CPLEX, GLPK, Gurobi, and SCIP and appears in datasets distributed by institutions such as DIMACS competitions and benchmark suites curated by NEOS Server and OR-Tools projects. The format’s simplicity makes it suitable for exchange among researchers affiliated with INFORMS, SIAM, and university labs at MIT, Stanford University, École Polytechnique, and University of Cambridge.

Format Specification

An LP file typically begins with an objective declaration (either "Minimize" or "Maximize"), followed by sections for "Subject To", "Bounds", "General", "Binary", and "End". The syntax supports linear expressions with coefficients, variable names, inequality signs, and optional named constraints. Implementations follow conventions influenced by standards appearing in publications from Bell Labs, IBM Research, and academic conferences like IFORS and EURO. Variable naming conventions often reflect practices from projects at Los Alamos National Laboratory and modeling efforts by researchers at Carnegie Mellon University and Princeton University.

Numerical representation adheres to floating-point conventions similar to those used in software from Intel Corporation and libraries such as BLAS and LAPACK. Commenting and whitespace rules are informal, which has led to dialects influenced by solver-specific parsers developed by teams at Zuse Institute Berlin and ETH Zurich.

Usage and Support

LP files are used to archive benchmark instances for competitions such as the COIN-OR Challenge and academic studies published in journals like Operations Research and Mathematical Programming. Large-scale industrial users include groups at Siemens, General Electric, and BP that integrate LP files into workflows involving SAP SE and Oracle Corporation enterprise systems. Educational materials at Harvard University, University of California, Berkeley, and University of Oxford use LP examples for coursework in optimization and courses co-developed with faculty who have contributed to INFORMS Journal on Computing.

Solver ecosystems provide import/export capabilities in tools developed by teams at Google (notably OR-Tools), IBM (notably CPLEX), and open-source communities around GNU projects and Netlib. Cloud platforms such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure support batch processing of LP files through workflow integrations created by research groups at Carnegie Mellon University and University of Illinois Urbana-Champaign.

Examples

Simple LP examples used in textbooks from Wiley and Springer illustrate canonical problems like diet optimization, production planning, and resource allocation. Benchmark examples from MIPLIB and test sets inspired by work at CNRS and INRIA include named instances with thousands of variables and constraints. Educational repositories maintained by Khan Academy style initiatives and course pages at University of Michigan host pedagogical LP files demonstrating objective minimization and maximization patterns.

Canonical example fragments often mirror formulations found in classic literature by authors affiliated with George Dantzig's lineage and subsequent surveys presented at IFORS meetings and SIAM Conference on Optimization sessions.

Tools and Software

Editors and integrated development environments that recognize LP syntax include extensions maintained in communities around Visual Studio Code, Eclipse, and Sublime Text. Conversion utilities and parsers are implemented in languages with strong numerical ecosystems such as Python (programming language) (via libraries connected to SciPy), Julia (programming language) (in packages developed by contributors linked to JuliaOpt), and C++ libraries used by teams at Google and IBM Research. Solver front-ends such as AMPL, Pyomo, and CVXOPT provide facilities to export models to LP files for interoperability with Gurobi and GLPK.

Repositories hosting tools include projects on platforms associated with GitHub and package managers like PyPI and Conda, where community members from institutions like University of Washington and ETH Zurich contribute parsers and validators.

History and Development

The LP format evolved during the late 20th century amid collaborations between industrial research labs such as Bell Labs and academic groups around Stanford University and University of California, Berkeley. Early adoption was driven by commercial solver vendors including IBM and later expanded through open-source movements exemplified by GNU Project and consortium efforts like COIN-OR. Key milestones coincide with the rise of optimization competitions at DIMACS and the establishment of repositories such as MIPLIB curated by researchers at Zuse Institute Berlin and ETH Zurich. Ongoing development reflects contributions from solver developers at IBM Research, Gurobi Optimization, and academic centers at MIT and University of Cambridge.

Category:Computer file formats