Derivative-Free and Blackbox Optimization

Nonfiction, Science & Nature, Mathematics, Number Systems, Applied
Cover of the book Derivative-Free and Blackbox Optimization by Charles Audet, Warren Hare, Springer International Publishing
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Author: Charles Audet, Warren Hare ISBN: 9783319689135
Publisher: Springer International Publishing Publication: December 2, 2017
Imprint: Springer Language: English
Author: Charles Audet, Warren Hare
ISBN: 9783319689135
Publisher: Springer International Publishing
Publication: December 2, 2017
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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