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numerical-optimization

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Practical optimization — choosing algorithms, handling constraints, tuning, and diagnosing convergence failures.

skills/numerical-optimization/SKILL.md

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## Overview

Optimization is everywhere in science: fitting models, designing
experiments, training ML, calibrating simulations. This skill covers
selecting the right algorithm for the problem structure (smooth vs
non-smooth, constrained vs free, convex vs non-convex), setting up
objectives correctly (scaling, gradients), and diagnosing the failures
that waste weeks of compute.

## When to use

- Fitting a model to data (least squares, maximum likelihood)
- Choosing between gradient-based, derivative-free, and global optimizers
- Handling constraints: bounds, equalities, inequalities
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