Optimization

Optimization is the quiet engine behind modern AI. It's what makes learning happen, what turns raw ideas into working systems, and what separates "it runs" from "it performs." If you've ever wondered why gradient descent works, how constraints change the game, or when you should stop chasing derivatives and switch to black-box search-this book gives you a clear, intuition-first path to real understanding.

Part of The 99-Page AI Lab series, Optimization compresses the essentials into a focused, hands-on sprint built for self-learners, busy practitioners, and students who want mastery without textbook bloat. You'll learn how to think like an optimizer: how to define the knobs you can control, what "better" truly means, how to respect real-world limits, and how to choose an algorithm family that matches the problem in front of you.

Inside you'll learn:

• What optimization really is (and what you're actually "minimizing")
• How to formulate problems clearly: variables, objectives, constraints, and trade-offs
• Gradient-based optimization in practice: learning rates, momentum, tuning, and failure modes
• Convexity and constraints-why they matter and how they show up in real projects
• Stochastic methods and why noise can be a feature, not a bug
• Black-box optimization, metaheuristics, and when they make sense
• The most common pitfalls that derail results-and how to avoid them
• Exercises and studio-style projects that build confidence, then stretch your ability

Written by Professor Seyedali Mirjalili, a widely recognized researcher in optimization and artificial intelligence, this book is designed to be fast to finish, hard to forget, and useful for years as a compact reference you'll actually return to.