Link Download ebook Free: https://ouo.io/kmrswQ Derivative-Free and Blackbox Optimization By: Charles Audet; Warren Hare Publisher: Springer Print ISBN: 9783319689128, 3319689126 eText ISBN: 9783319689135, 3319689134 Copyright year: 2017 Format: EPUB Available from $ 69.99 USD SKU 9783319689135 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.
