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highs

Solve large-scale LP, MILP, and QP problems with HiGHS solvers.

1|Updated Oct 1, 2025
Search Tags:#API#optimization#GPU#LP#HiGHS#MILP#QP
One-click install
npx skills add https://github.com/sverzijl/planning_latest --skill highs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: highs
Source: https://github.com/sverzijl/planning_latest/tree/main/.claude/skills/highs
Command: npx skills add https://github.com/sverzijl/planning_latest --skill highs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

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.

Frequently Asked Questions about highs

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I solve large-scale LP and MILP optimization problems in Python?▼

You can solve large-scale LP and MILP problems in Python using the HiGHS framework, which provides robust Python language bindings alongside Julia, C/C++, C#, and Rust for defining and deploying optimal decisions.

What is the best way to configure solver selection for linear and quadratic programming?▼

The best way to configure solver selection is using a versatile optimization suite that offers simplex, interior-point, and PDLP for LP, MIP for MILP, and active-set methods for QP to target specific problem characteristics.

Can I use GPU acceleration for large linear programming models?▼

Yes, you can leverage GPU acceleration when available for large linear programming models to achieve fast, scalable optimization and improved performance tuning during production deployment.

Does this optimization framework support Rust and C# APIs?▼

Yes, the optimization framework supports cross-language APIs including Rust and C#, allowing developers to build and solve large-scale linear and quadratic optimization models across multiple programming environments.

What are the limitations of active-set methods for quadratic programming?▼

The metadata does not specify limitations of active-set methods for quadratic programming, but it confirms the framework uses active-set methods for QP and robust implementations of simplex, interior-point, and PDLP for LP.

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