nonlinear-optimization-solver

Solve general nonlinear optimization problems

509 stars

Best use case

nonlinear-optimization-solver is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Solve general nonlinear optimization problems

Teams using nonlinear-optimization-solver should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/nonlinear-optimization-solver/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/mathematics/skills/nonlinear-optimization-solver/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/nonlinear-optimization-solver/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How nonlinear-optimization-solver Compares

Feature / Agent	nonlinear-optimization-solver	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Solve general nonlinear optimization problems

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Nonlinear Optimization Solver

## Purpose

Provides capabilities for solving general nonlinear optimization problems including constrained and unconstrained formulations.

## Capabilities

- Gradient-based methods (BFGS, L-BFGS, CG)
- Newton and quasi-Newton methods
- Interior point methods
- Sequential quadratic programming (SQP)
- Global optimization (basin-hopping, differential evolution)
- Constraint handling

## Usage Guidelines

1. **Starting Point**: Provide good initial guesses
2. **Gradient Information**: Supply gradients when available
3. **Global vs Local**: Choose global methods for multimodal problems
4. **Constraint Handling**: Use appropriate constraint formulations

## Tools/Libraries

- IPOPT
- KNITRO
- NLopt
- scipy.optimize

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