Derivative-Free Optimization
SkillDev toolsOptimization without gradient information
Use Derivative-Free Optimization in Claude, ChatGPT or Ahel Desktop
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Details
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.
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Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
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
- 113
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
derivative-free-optimization- Source
- github.com/a5c-ai/babysitter