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.
| Format |
Inbunden |
| Omfång |
425 sidor |
| Språk |
Engelska |
| Förlag |
Springer Nature Switzerland AG |
| Utgivningsdatum |
2026-06-18 |
| ISBN |
9783032009050 |
