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Derivative free optimization

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

Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration

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

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Optimization without gradient information

SKILL.md

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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

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