Derivative-Free Optimization
SkillDev toolsOptimization without gradient information
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Derivative-Free Optimization skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/mathematics/skills/derivative-free-optimization/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
derivative-free-optimization- Source
- github.com/a5c-ai/babysitter