AI4Math · Optimization

Skill packages for mathematical optimization modeling, solver setup, LP/MIP/SOCP workflows, and manifold-constrained optimization.

中文说明 · Contributors · Skill packages · Installation · Quick start · Security model

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What This Repository Is

This repository is the AI4Math home for optimization skills. It collects packages for modeling mathematical programs, selecting solvers, checking solver environments, adapting examples, and reporting evidence from optimization runs.

Use the root page as the public map. For concrete modeling or solving, open the package that matches the problem class.

Skill Packages

Package	Use it for	Start here
cdopt-optimization	CDOpt and manifold-constrained optimization modeling, validation, runner generation, and evidence reports.	README · SKILL
copt-linear-program	COPT/coptpy linear-programming workflows, word-problem restatement, and solver scripts.	README · SKILL
linear-programming	General LP modeling, solver selection, and natural-language-to-model workflows.	README · SKILL
mixed-integer-programming	MILP/MIP modeling with binary, integer, and continuous decision variables.	README · SKILL
second-order-cone-programming	SOCP modeling and cvxpy-based conic solver workflows.	README · SKILL
or-solver	Shared solver detection, installation planning, license checks, and solver selection for OR skills.	README · SKILL

Installation

The recommended path is AI-assisted installation: ask your coding agent to clone or update this repository, read the Skill instructions, install the entrypoints, and verify discovery.

Please install these AI4Math Skills for me.

Repository: https://github.com/VeryMath/AI4Math-Optimization.git
Branch: main
Skill paths:
- skills/cdopt-optimization
- skills/copt-linear-program
- skills/linear-programming
- skills/mixed-integer-programming
- skills/second-order-cone-programming
- skills/or-solver

Steps:
1. Clone or update the repository locally.
2. Read README.md, SKILL.md, AGENTS.md if present, and each target Skill entrypoint.
3. If this environment supports local Skill discovery, link each directory that contains SKILL.md into the local skills directory.
4. Keep shared sibling support directories in place when a Skill depends on them.
5. Verify that the installed Skills are discoverable.
6. Tell me the installed paths, whether a restart is needed, and give me one test prompt.

Manual fallback for Codex-style local discovery:

git clone https://github.com/VeryMath/AI4Math-Optimization.git
cd AI4Math-Optimization
mkdir -p ~/.codex/skills
ln -s "$PWD/skills/cdopt-optimization" ~/.codex/skills/cdopt-optimization
ln -s "$PWD/skills/copt-linear-program" ~/.codex/skills/copt-linear-program
ln -s "$PWD/skills/linear-programming" ~/.codex/skills/linear-programming
ln -s "$PWD/skills/mixed-integer-programming" ~/.codex/skills/mixed-integer-programming
ln -s "$PWD/skills/second-order-cone-programming" ~/.codex/skills/second-order-cone-programming
ln -s "$PWD/skills/or-solver" ~/.codex/skills/or-solver

If your agent uses a different local Skill directory, replace ~/.codex/skills with that configured path.

Quick Start

Clone the repository and choose a package:

git clone https://github.com/VeryMath/AI4Math-Optimization.git
cd AI4Math-Optimization

For solver setup and selection, start with:

skills/or-solver/SKILL.md

For general LP/MIP/SOCP modeling, start with the matching package under skills/. For CDOpt or manifold-constrained tasks, start with:

skills/cdopt-optimization/SKILL.md

Repository Layout

AI4Math-Optimization/
├── README.md
├── README.zh-CN.md
├── SKILL.md
└── skills/
    ├── cdopt-optimization/
    ├── copt-linear-program/
    ├── linear-programming/
    ├── mixed-integer-programming/
    ├── or-solver/
    └── second-order-cone-programming/

Keep solver examples, scripts, references, and generated run evidence inside the package that owns them.

Validation

There is no root build step. When changing a package, validate its SKILL.md, README links, scripts, and package-local examples. If you use Codex's local skill validator, run it against every changed standard skill package.

Security and Scope

Do not commit solver licenses, API keys, private datasets, .env files, generated solver logs with sensitive data, or local run outputs. Public examples should include source notes for benchmark data and be safe to redistribute.