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convex-optimization

Solve convex optimization problems using SciPy and Z3 solvers.

3.9k|296|Updated Dec 23, 2025
Search Tags:#optimization#kkt conditions#linear programming#scipy#convexity#quadratic programming
One-click install
npx skills add https://github.com/parcadei/Continuous-Claude-v3 --skill convex-optimization-parcadei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: convex-optimization
Source: https://github.com/parcadei/Continuous-Claude-v3/tree/main/.claude/skills/math/optimization/convex-optimization
Command: npx skills add https://github.com/parcadei/Continuous-Claude-v3 --skill convex-optimization-parcadei

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires z3-solver, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a structured approach to solving convex optimization problems, guiding users through verification, classification, and solution methods.

Core Features & Use Cases

  • Convexity Verification: Helps determine if an objective function and constraint set are convex.
  • Problem Classification: Identifies the type of convex problem (Linear, Quadratic, General, Semidefinite) to select appropriate solvers.
  • KKT Conditions: Assists in understanding and verifying the Karush-Kuhn-Tucker conditions for optimality.
  • Use Case: When faced with a complex optimization task, use this Skill to systematically check if it's a convex problem and then apply the correct mathematical and computational tools for an efficient solution.

Quick Start

Use the convex-optimization skill to solve a linear programming problem with the given constraints.

Frequently Asked Questions about convex-optimization

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

FAQPage Schema
How do I verify convexity for an objective function and constraint set?▼

Convexity verification involves checking if the objective function and constraint set satisfy convex properties. This Skill systematically tests these conditions to determine if the problem is convex before selecting a solver.

How do I solve linear programming problems using Python and SciPy?▼

Solving linear programming with SciPy requires classifying the problem type and applying the correct solver. This Skill identifies the problem as linear and uses SciPy to compute the optimal solution under the given constraints.

What are KKT conditions and how do I verify them for optimization optimality?▼

KKT (Karush-Kuhn-Tucker) conditions are mathematical criteria for optimality in constrained convex optimization. This Skill assists in understanding and verifying these conditions to confirm that a computed solution is truly optimal.

Can I use Z3 solver to verify solutions for quadratic programming problems?▼

Yes, Z3 solver can verify solutions for quadratic programming and other convex problems. This Skill utilizes Z3 as a symbolic solver alongside SciPy to verify the correctness and optimality of computed solutions.

Does this approach work for semidefinite programming and general convex problems?▼

This approach supports semidefinite programming and general convex problems by classifying the problem type. It identifies the correct category to select appropriate mathematical and computational tools for an efficient solution.

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