Derivative-Free and Blackbox Optimization
Series: Springer Series in Operations Research and Financial Engineering;
- Publisher's listprice EUR 69.54
-
28 841 Ft (27 468 Ft + 5% VAT)
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28 841 Ft
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Product details:
- Edition number 2
- Publisher Springer Nature Switzerland
- Date of Publication 4 February 2026
- Number of Volumes 1 pieces, Book
- ISBN 9783032009050
- Binding Hardback
- No. of pages489 pages
- Size 235x155 mm
- Language English
- Illustrations X, 489 p. 86 illus., 78 illus. in color. Illustrations, black & white 700
Categories
Long description:
"
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.
Flexible usage suitable for undergraduate, graduate, mathematics, computer science, engineering, or mixed classes
15 end-of-chapter projects are provided, allowing advanced exploration of desired topics
Includes numerous exercises throughout to test knowledge and advance understanding" MoreTable of Contents:
Part 1. Introduction and Background Material.- Introduction: Tools and Challenges in Derivative-Free and Blackbox Optimization.- Mathematical Background.- The Beginnings of DFO Algorithms.- Comparing Optimization Methods.- Some Remarks on DFO.- Part 2. Popular Heuristic Methods.- Genetic Algorithms.- Nelder-Mead.- Further Remarks on Heuristics.- Part 3. Direct Search Methods.- Positive Bases and Nonsmooth Optimization.- Generalised Pattern Search.- Mesh Adaptive Direct Search.- Variables and Constraints.- Further Remarks on Direct Search Methods.- Part 4. Model-Based Methods.- Assessing Model Quality.- Simplex Gradients and Hessians.- Model-Based Descent.- Model-Based Trust Region.- Further Remarks on Model-Based Methods.- Part 5. Extensions and Refinements.- Optimization Using Surrogates and Models.- Biobjective Optimization.- Final Remarks on DFO/BBO.- Appendix A. Blackbox Test Problems.- Appendix. Answers to Every Fourth Exercise.- Bibliography.- Index.
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