Derivative-Free and Blackbox Optimization (Springer Series in Operations Research and Financial Engineering) - Hardcover

Audet, Charles; Hare, Warren

Hardcover

ISBN 10: 3032009057 ISBN 13: 9783032009050

Publisher: Springer, 2026

Synopsis

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. 

About this title

About the Author

 

Dr. Charles Audet is a Professor of Mathematics at the École Polytechnique de Montréal. His research interests include the analysis and development of algorithms for blackbox nonsmooth optimization, and structured global optimization. He obtained a Ph.D. degree in applied mathematics from the École Polytechnique de Montréal, and worked as a postdoctoral researcher at Rice University.

Dr. Warren Hare is a Professor of Mathematics at the University of British Columbia, Okanagan Campus.  His research interests include numerical analysis and algorithm design, particularly for derivative-free optimisation.  He obtained his Ph.D. in optimization from Simon Fraser University and worked as postdoctoral researcher at the Instituto de Mathemática Pura e Applicada and McMaster University.

 

 

 

From the Back Cover

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. 

Edition details

• Publisher: Springer
• Publication date: 2026
• Language: English
• ISBN 10: 3032009057
• ISBN 13: 9783032009050
• Binding: Hardcover
• Edition number: 2
• Number of pages: 448