Derivative-free and blackbox optimization / Charles Audet, Warren Hare.
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...
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| Main Authors: | , |
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| Format: | Ebook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
2017.
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| Series: | Springer series in operations research,
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| Subjects: | |
| Online Access: | Springer eBooks |
| Summary: | 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. |
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| Physical Description: | 1 online resource (xviii, 302 pages) : illustrations. |
| Bibliography: | Includes bibliographical references and index. |
| ISSN: | 1431-8598 |