convex-optimization

Verify convexity, classify problem types, and select solvers for SciPy and Z3.

3|Updated Jan 29, 2026

Search Tags:#optimization #kkt conditions #convex optimization #z3 #scipy #mathematical programming #solver selection

Ready to run

npx skills add https://github.com/LunchTable-TCG/LTCG --skill convex-optimization

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: convex-optimization
Source: https://github.com/LunchTable-TCG/LTCG/tree/main/.claude/skills/convex-optimization
Command: npx skills add https://github.com/LunchTable-TCG/LTCG --skill convex-optimization

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides strategies and tools for solving convex optimization problems, streamlining the process of finding optimal solutions within defined constraints.

Core Features & Use Cases

Problem Verification: Helps verify if a problem is convex by checking objective functions and constraint sets.
Solver Selection: Guides the choice of appropriate solvers based on problem classification (Linear, Quadratic, General Convex, Semidefinite).
Standard Form Conversion: Assists in converting optimization problems into standard forms for solvers.
KKT Conditions: Aids in understanding and verifying Karush-Kuhn-Tucker (KKT) conditions.
Use Case: When faced with a complex optimization task in machine learning or operations research, use this skill to systematically approach the problem, select the right tools, and verify the solution.

Quick Start

Use the convex-optimization skill to solve a linear programming problem with the provided parameters.

Frequently Asked Questions about convex-optimization

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

FAQPage Schema

How do I verify if my optimization problem is convex?▼

To verify convex optimization problems, you must check the convexity of both the objective function and the constraint sets. This process ensures the problem meets standard forms before applying optimization solvers.

How do I select the right solver for mathematical programming in SciPy or Z3?▼

Solver selection for mathematical programming depends on classifying the problem type as Linear, Quadratic, General Convex, or Semidefinite. Matching this classification with the appropriate tool commands guides the choice between SciPy and Z3.

What are KKT conditions and how are they used in convex optimization?▼

KKT conditions are mathematical criteria used to verify optimal solutions in convex optimization. They define the necessary steps for checking whether a given solution satisfies the constraints and objective function requirements.

Can I use this approach to convert a machine learning objective into a standard form for solvers?▼

Yes, standard form conversion transforms machine learning objectives into formats compatible with mathematical solvers. This allows systematic problem solving by structuring complex models into recognized convex optimization frameworks.

Does this method support operations research tasks with complex constraint sets?▼

Yes, solving convex optimization problems supports operations research tasks by systematically evaluating constraint sets. It classifies the problem type and selects appropriate solvers to find robust solutions within defined limits.