Derivative - Free and Blackbox Optimization 2nd Edition
Category: eBooks
- views: 123
- date: 19 June 2026
- posted by: BeMyLove
DerivativFree and Blackbox Optimization 2nd Edition | 18.44 MB
Title: DerivativFree and Blackbox Optimization 2nd Edition
Author: Charles Audet • Warren Hare
Category: Nonfiction, Science & Nature, Mathematics
Language: English | 436 Pages | ISBN: 9783319689135
Description:
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.
DOWNLOAD:
https://rapidgator.net/file/1e8223f8aa1c78c61cea1f4e51d0e599/Derivative-Free_and_Blackbox_Optimization_2nd_Edition.rar
https://nitroflare.com/view/9D5CEFBE1CFCE8C/Derivative-Free_and_Blackbox_Optimization_2nd_Edition.rar
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.
DOWNLOAD:
https://rapidgator.net/file/1e8223f8aa1c78c61cea1f4e51d0e599/Derivative-Free_and_Blackbox_Optimization_2nd_Edition.rar
https://nitroflare.com/view/9D5CEFBE1CFCE8C/Derivative-Free_and_Blackbox_Optimization_2nd_Edition.rar
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