ReddRadar
Agent skill
derivative-free-optimization
Optimization without gradient information
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/science/mathematics/skills/derivative-free-optimization
Metadata
Additional technical details for this skill
- phase
- 6
- domain
- science
- category
- optimization
- specialization
- mathematics
SKILL.md
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
- Method Selection: Choose based on problem characteristics
- Function Evaluations: Minimize expensive function calls
- Surrogate Models: Build and refine surrogate approximations
- Exploration-Exploitation: Balance search strategies
Tools/Libraries
- scipy.optimize
- Optuna
- GPyOpt
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