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Derivative free optimization

Skill a5c-ai/babysitter/library/specializations/domains/science/mathematics/skills/derivative-free-optimization

Optimization without gradient informationFrom its SKILL.md

Install
npx -y skills add a5c-ai/babysitter --skill derivative-free-optimization

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

1.2 KB, 135 tokens by cl100k_base, as published. Nobody here has run it

Derivative-Free Optimization

Purpose

Provides optimization capabilities for problems where gradient information is unavailable or unreliable.

Capabilities

  • Nelder-Mead simplex method
  • Powell's method
  • Surrogate-based optimization
  • Bayesian optimization
  • Pattern search methods
  • Trust region methods

Usage Guidelines

  1. Method Selection: Choose based on problem characteristics
  2. Function Evaluations: Minimize expensive function calls
  3. Surrogate Models: Build and refine surrogate approximations
  4. Exploration-Exploitation: Balance search strategies

Tools/Libraries

  • scipy.optimize
  • Optuna
  • GPyOpt

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

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