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Viser: Derivative-Free and Blackbox Optimization
Derivative-Free and Blackbox Optimization
Charles Audet og Warren Hare
(2018)
Sprog: Engelsk
om ca. 15 hverdage
Detaljer om varen
- Paperback
- Udgiver: Springer International Publishing AG (September 2018)
- Forfattere: Charles Audet og Warren Hare
- ISBN: 9783319886800
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.
Part I: Introduction and Background Material.- Introduction: Tools and Challenges.- Mathematical Background.- The Beginnings of DFO Algorithms.-
Part I: Some Remarks on DFO.-
Part II: Popular Heuristic Methods.- Genetic Algorithms.- Nelder-Mead.-
Part II: Further Remarks on Heuristics.-
Part III: Direct Search Methods.- Positive bases and Nonsmooth Optimization.- Generalized Pattern Search.- Mesh Adaptive Direct Search.-
Part III: Further Remarks on Direct Search Methods.-
Part IV: Model-based Methods.- Model-based Descent.- Model-based Trust Region.-
Part IV: Further Remarks on Model-based Methods.-
Part V: Extensions and Refinements.- Variables and Constraints.- Optimization Using Surrogates and Models.- Biobjective Optimization.-
Part V: Final Remarks on DFO/BBO.-
Part VI: Appendix: Comparing Optimization Methods.- Solutions to Selected Exercises.
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