> ## Documentation Index
> Fetch the complete documentation index at: https://math.aboneda.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction to Optimization

> Foundations of mathematical optimization

# Optimization

Optimization is the mathematical discipline of finding the best solution from a set of feasible alternatives. It is central to machine learning, operations research, economics, and engineering.

## Key Topics

* **Unconstrained Optimization** — gradient descent, Newton's method, convexity
* **Constrained Optimization** — Lagrange multipliers, KKT conditions
* **Linear Programming** — simplex method, duality, sensitivity analysis
* **Convex Optimization** — convex sets, convex functions, disciplined convex programming
* **Integer Programming** — branch and bound, cutting planes
* **Dynamic Programming** — Bellman's principle of optimality
* **Stochastic Optimization** — stochastic gradient descent, Monte Carlo methods
* **Multi-objective Optimization** — Pareto optimality, trade-off analysis