highs
CommunityFast, scalable optimization for LP/MILP/QP.
Authorsverzijl
Version1.0.0
Installs0
System Documentation
What problem does it solve?
HiGHS provides a fast, scalable framework for defining and solving large-scale linear and quadratic optimization problems (LP, MILP, and QP), helping teams model, solve, and deploy optimal decisions.
Core Features & Use Cases
- Cross-language APIs: Python, Julia, C/C++, C#, and Rust interfaces for building and solving optimization models.
- Versatile solvers: robust implementations of simplex, interior-point, and PDLP for LP; MIP for MILP; active-set methods for QP.
- Getting started and deployment: install from source or package managers, configure options, and leverage GPU acceleration when available for large problems.
- Use case: model a production planning LP and obtain an optimal production plan across multiple periods and facilities.
Quick Start
Run the HiGHS Python quickstart to define a tiny LP, solve it, and print the results.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: highs Download link: https://github.com/sverzijl/planning_latest/archive/main.zip#highs Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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