Derivative-free and blackbox optimization

• 作者： Audet, Charles, author.
• 其他作者：
  • Hare, Warren., author.
  • SpringerLink (Online service)
• 其他題名：
  • Springer series in operations research and financial engineering.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Springer series in operations research and financial engineering,
• 主題： Mathematical optimization. , Mathematics. , Optimization. , Numerical Analysis.
• ISBN： 9783319689135 (electronic bk.) 、 9783319689128 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: 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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• 摘要註: 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.
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