Derivative-Free and Blackbox Optimization

The second edition of Derivative-Free and Blackbox Optimization offers a comprehensive introduction to the field of optimization when derivatives are unavailable, unreliable, or impractical.Whether you’re a student, instructor, or self-learner, this book is designed to guide you through both the foundations and advanced techniques of derivative-free and blackbox optimization.This new edition features significantly expanded exercises, updated and intuitive notation, over 30 new figures, and a wide range of pedagogical enhancements aimed at making complex concepts accessible and engaging.The book is structured into five parts. Part 1 established foundational principles, including an expanded chapter on proper benchmarking.Parts 2, 3, and 4, take an in-depth look at heuristics, direct search, and model based approaches (respectively).Part 5 extends these approaches to specialised settings.Finally, a new appendix contributed by Sébastien Le Digabel, details several real-world applications of blackbox optimization, and links to software for each application.Whether used in the classroom or for independent exploration, this book is a powerful resource for understanding and applying optimization methods – no gradients required.