Derivative-Free and Blackbox Optimization

• Κύριοι συγγραφείς: Audet, Charles (Συγγραφέας), Hare, Warren (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: Springer Series in Operations Research and Financial Engineering,
• Θέματα: Mathematics.; Numerical analysis.; Mathematical optimization.; Optimization.; Numerical Analysis.

 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.