Numerical nonsmooth optimization: state of the art algorithms
Introduction -- Part I: General Methods -- Part II: Structure Exploiting Methods -- Part III: Methods for Special Problems -- Part IV: Derivative-free Methods
Gespeichert in:
| Weitere Verfasser: | , , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Springer
[2020]
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| Schlagworte: | |
| Zusammenfassung: | Introduction -- Part I: General Methods -- Part II: Structure Exploiting Methods -- Part III: Methods for Special Problems -- Part IV: Derivative-free Methods Solving nonsmooth optimization (NSO) problems is critical in many practical applications and real-world modeling systems. The aim of this book is to survey various numerical methods for solving NSO problems and to provide an overview of the latest developments in the field. Experts from around the world share their perspectives on specific aspects of numerical NSO. The book is divided into four parts, the first of which considers general methods including subgradient, bundle and gradient sampling methods. In turn, the second focuses on methods that exploit the problem’s special structure, e.g. algorithms for nonsmooth DC programming, VU decomposition techniques, and algorithms for minimax and piecewise differentiable problems. The third part considers methods for special problems like multiobjective and mixed integer NSO, and problems involving inexact data, while the last part highlights the latest advancements in derivative-free NSO. Given its scope, the book is ideal for students attending courses on numerical nonsmooth optimization, for lecturers who teach optimization courses, and for practitioners who apply nonsmooth optimization methods in engineering, artificial intelligence, machine learning, and business. Furthermore, it can serve as a reference text for experts dealing with nonsmooth optimization |
| Beschreibung: | xvii, 698 Seiten Illustrationen |
| ISBN: | 9783030349097 |
Internformat
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| 245 | 1 | 0 | |a Numerical nonsmooth optimization |b state of the art algorithms |c Adil M. Bagirov, Manlio Gaudioso, Napsu Karmitsa, Marko M. Mäkelä, Sona Taheri, editors |
| 264 | 1 | |a Cham, Switzerland |b Springer |c [2020] | |
| 300 | |a xvii, 698 Seiten |b Illustrationen | ||
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| 520 | 3 | |a Introduction -- Part I: General Methods -- Part II: Structure Exploiting Methods -- Part III: Methods for Special Problems -- Part IV: Derivative-free Methods | |
| 520 | 3 | |a Solving nonsmooth optimization (NSO) problems is critical in many practical applications and real-world modeling systems. The aim of this book is to survey various numerical methods for solving NSO problems and to provide an overview of the latest developments in the field. Experts from around the world share their perspectives on specific aspects of numerical NSO. The book is divided into four parts, the first of which considers general methods including subgradient, bundle and gradient sampling methods. In turn, the second focuses on methods that exploit the problem’s special structure, e.g. algorithms for nonsmooth DC programming, VU decomposition techniques, and algorithms for minimax and piecewise differentiable problems. The third part considers methods for special problems like multiobjective and mixed integer NSO, and problems involving inexact data, while the last part highlights the latest advancements in derivative-free NSO. Given its scope, the book is ideal for students attending courses on numerical nonsmooth optimization, for lecturers who teach optimization courses, and for practitioners who apply nonsmooth optimization methods in engineering, artificial intelligence, machine learning, and business. Furthermore, it can serve as a reference text for experts dealing with nonsmooth optimization | |
| 653 | 0 | |a Operations research | |
| 653 | 0 | |a Management science | |
| 653 | 0 | |a Decision making | |
| 653 | 0 | |a Numerical analysis | |
| 653 | 0 | |a Data mining | |
| 653 | 0 | |a Economic theory | |
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 700 | 1 | |a Bagirov, Adil |0 (DE-588)138953643 |4 edt | |
| 700 | 1 | |a Gaudioso, Manlio |d 1949- |0 (DE-588)1202720765 |4 edt | |
| 700 | 1 | |a Karmitsa, Napsu |d 1971- |0 (DE-588)1117954625 |4 edt | |
| 700 | 1 | |a Mäkelä, Marko M. |0 (DE-588)1030008485 |4 edt | |
| 700 | 1 | |a Taheri, Sona |0 (DE-588)1211581160 |4 edt | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-030-34910-3 |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032653711 | ||
Datensatz im Suchindex
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| building | Verbundindex |
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| ctrlnum | (OCoLC)1190678544 (DE-599)KXP1700092561 |
| dewey-full | 519.6 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
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| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| discipline_str_mv | Mathematik |
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| isbn | 9783030349097 |
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| spelling | Numerical nonsmooth optimization state of the art algorithms Adil M. Bagirov, Manlio Gaudioso, Napsu Karmitsa, Marko M. Mäkelä, Sona Taheri, editors Cham, Switzerland Springer [2020] xvii, 698 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Introduction -- Part I: General Methods -- Part II: Structure Exploiting Methods -- Part III: Methods for Special Problems -- Part IV: Derivative-free Methods Solving nonsmooth optimization (NSO) problems is critical in many practical applications and real-world modeling systems. The aim of this book is to survey various numerical methods for solving NSO problems and to provide an overview of the latest developments in the field. Experts from around the world share their perspectives on specific aspects of numerical NSO. The book is divided into four parts, the first of which considers general methods including subgradient, bundle and gradient sampling methods. In turn, the second focuses on methods that exploit the problem’s special structure, e.g. algorithms for nonsmooth DC programming, VU decomposition techniques, and algorithms for minimax and piecewise differentiable problems. The third part considers methods for special problems like multiobjective and mixed integer NSO, and problems involving inexact data, while the last part highlights the latest advancements in derivative-free NSO. Given its scope, the book is ideal for students attending courses on numerical nonsmooth optimization, for lecturers who teach optimization courses, and for practitioners who apply nonsmooth optimization methods in engineering, artificial intelligence, machine learning, and business. Furthermore, it can serve as a reference text for experts dealing with nonsmooth optimization Operations research Management science Decision making Numerical analysis Data mining Economic theory (DE-588)4143413-4 Aufsatzsammlung gnd-content Bagirov, Adil (DE-588)138953643 edt Gaudioso, Manlio 1949- (DE-588)1202720765 edt Karmitsa, Napsu 1971- (DE-588)1117954625 edt Mäkelä, Marko M. (DE-588)1030008485 edt Taheri, Sona (DE-588)1211581160 edt Erscheint auch als Online-Ausgabe 978-3-030-34910-3 |
| spellingShingle | Numerical nonsmooth optimization state of the art algorithms |
| subject_GND | (DE-588)4143413-4 |
| title | Numerical nonsmooth optimization state of the art algorithms |
| title_auth | Numerical nonsmooth optimization state of the art algorithms |
| title_exact_search | Numerical nonsmooth optimization state of the art algorithms |
| title_exact_search_txtP | Numerical nonsmooth optimization state of the art algorithms |
| title_full | Numerical nonsmooth optimization state of the art algorithms Adil M. Bagirov, Manlio Gaudioso, Napsu Karmitsa, Marko M. Mäkelä, Sona Taheri, editors |
| title_fullStr | Numerical nonsmooth optimization state of the art algorithms Adil M. Bagirov, Manlio Gaudioso, Napsu Karmitsa, Marko M. Mäkelä, Sona Taheri, editors |
| title_full_unstemmed | Numerical nonsmooth optimization state of the art algorithms Adil M. Bagirov, Manlio Gaudioso, Napsu Karmitsa, Marko M. Mäkelä, Sona Taheri, editors |
| title_short | Numerical nonsmooth optimization |
| title_sort | numerical nonsmooth optimization state of the art algorithms |
| title_sub | state of the art algorithms |
| topic_facet | Aufsatzsammlung |
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