Derivative-Free and Blackbox Optimization

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Synopsis

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.

Book details

Series:
Springer Series in Operations Research and Financial Engineering
Author:
Charles Audet, Warren Hare
ISBN:
9783319689135
Related ISBNs:
9783319689128
Publisher:
Springer International Publishing
Pages:
N/A
Reading age:
Not specified
Includes images:
Yes
Date of addition:
2018-10-14
Usage restrictions:
Copyright
Copyright date:
2017
Copyright by:
Springer International Publishing, Cham 
Adult content:
No
Language:
English
Categories:
Mathematics and Statistics, Nonfiction