Derivative-Free and Blackbox Optimization
(Springer, 2017)
SCI 337
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.
(Description Source: Springer)
Authors
Warren Hare is a professor of Mathematics at the University of British Columbia Okanagan campus, which he joined in 2009. He received his PhD in Mathematical Optimization from Simon Fraser University. He completed his postdoctoral research at IMPA (Brazil) and McMaster (Canada). He serves as an Associate Editor with Set Valued and Variational Analysis and the Pacific Journal of Optimization. He is the co-author of the book Derivative-Free and Blackbox Optimization, and his research focuses on structured blackbox optimization.
Charles Audet is a professor of Mathematics at the École Polytechnique de Montréal.
UBC Library Holdings
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Paper ISBN: 9783319886800
Hardcover ISBN: 9783319689128
eBook ISBN: 9783319689135
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