name: derivative-free-optimization description: Optimization without gradient information allowed-tools:

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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