Authors:
- 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
Part of the book series: Springer Series in Operations Research and Financial Engineering (ORFE)
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Table of contents (14 chapters)
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Front Matter
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Introduction and Background Material
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Front Matter
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Popular Heuristic Methods
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Front Matter
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Direct Search Methods
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Front Matter
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Model-Based Methods
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Front Matter
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Extensions and Refinements
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Front Matter
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About this book
This book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization.
The book is split into 5 parts and is designed to be modular; any individual part depends only on the material in Part I. Part I of the book discusses what is meant by Derivative-Free and Blackbox Optimization, provides background material, and early basics while Part II focuses on heuristic methods (Genetic Algorithms and Nelder-Mead). Part III presents direct search methods (Generalized Pattern Search and Mesh Adaptive Direct Search) and Part IV focuses on model-based methods (Simplex Gradient and Trust Region). Part V discusses dealing with constraints, using surrogates, and bi-objective optimization.
End of chapter exercises are included throughout as well as 15 end of chapter projects and over 40 figures. Benchmarking techniques are also presented in the appendix.
Reviews
“This book targets two audiences: individuals interested in understanding derivative-free optimization (DFO) and blackbox optimization and practitioners who have to solve real-world problems that cannot be approached by traditional gradient-based methods. … The book is written in a clear style with sufficient details, examples and proofs of theoretical results. The authors pay equalattention to careful theoretical development and analysis of the methods, and to practical details of the algorithms.” (Olga Brezhneva, Mathematical Reviews, October, 2018)
“The authors present a comprehensive textbook being an introduction to blackbox and derivative- free optimization. … The book is for sure a necessary position for students of mathematics, IT or engineering that would like to explore the subject of blackbox and derivative-free optimization. Also the researchers in the area of optimization could treat it as an introductory reading. Finally, the book would be also a good choice for practitionners dealing with such kind of problems.” (Marcin Anholcer, zbMATH 1391.90001, 2018)
Authors and Affiliations
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Dépt. Mathématiques et Génie Industriel, Ecole Polytechnique de Montréal, Montréal, Canada
Charles Audet
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Department of Mathematics, University of British Columbia, Kelowna, Canada
Warren Hare
About the authors
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 post-doc at Rice University in Houston, Texas.
Dr. Warren Hare received his Ph.D. in Mathematical Optimization from Simon Fraser University. He complete postdoctoral research at IMPA (Brazil) and McMaster (Canada), before joining the University of British Columbia (Canada).
Bibliographic Information
Book Title: Derivative-Free and Blackbox Optimization
Authors: Charles Audet, Warren Hare
Series Title: Springer Series in Operations Research and Financial Engineering
DOI: https://doi.org/10.1007/978-3-319-68913-5
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-68912-8Published: 13 December 2017
Softcover ISBN: 978-3-319-88680-0Published: 04 September 2018
eBook ISBN: 978-3-319-68913-5Published: 02 December 2017
Series ISSN: 1431-8598
Series E-ISSN: 2197-1773
Edition Number: 1
Number of Pages: XVIII, 302
Number of Illustrations: 38 b/w illustrations
Topics: Optimization, Numerical Analysis