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978-3-319-68913-5 |
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171202s2017 gw | s |||| 0|eng d |
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|a 9783319689135
|9 978-3-319-68913-5
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|a 10.1007/978-3-319-68913-5
|2 doi
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|a Audet, Charles.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Derivative-Free and Blackbox Optimization
|h [electronic resource] /
|c by Charles Audet, Warren Hare.
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|a 1st ed. 2017.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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|a XVIII, 302 p. 38 illus.
|b online resource.
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|a text
|b txt
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|a computer
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|2 rdamedia
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|a online resource
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|a text file
|b PDF
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|a Springer Series in Operations Research and Financial Engineering,
|x 1431-8598
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|a 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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|a 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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|a Mathematical optimization.
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|a Numerical analysis.
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|a Optimization.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M26008
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|a Numerical Analysis.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M14050
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| 700 |
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|a Hare, Warren.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 710 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer Nature eBook
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| 776 |
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|i Printed edition:
|z 9783319689128
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| 776 |
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|i Printed edition:
|z 9783319689142
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| 776 |
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|i Printed edition:
|z 9783319886800
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| 830 |
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|a Springer Series in Operations Research and Financial Engineering,
|x 1431-8598
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|u https://doi.org/10.1007/978-3-319-68913-5
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|a Mathematics and Statistics (SpringerNature-11649)
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| 950 |
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|a Mathematics and Statistics (R0) (SpringerNature-43713)
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