Large-scale convex optimization: algorithms & analyses via monotone operators
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge, United Kingdom
Cambridge University Press
2023
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| Schlagwörter: | |
| Links: | https://doi.org/10.1017/9781009160865 https://doi.org/10.1017/9781009160865 |
| Abstract: | Starting from where a first course in convex optimization leaves off, this text presents a unified analysis of first-order optimization methods – including parallel-distributed algorithms – through the abstraction of monotone operators. With the increased computational power and availability of big data over the past decade, applied disciplines have demanded that larger and larger optimization problems be solved. This text covers the first-order convex optimization methods that are uniquely effective at solving these large-scale optimization problems. Readers will have the opportunity to construct and analyze many well-known classical and modern algorithms using monotone operators, and walk away with a solid understanding of the diverse optimization algorithms. Graduate students and researchers in mathematical optimization, operations research, electrical engineering, statistics, and computer science will appreciate this concise introduction to the theory of convex optimization algorithms. |
| Umfang: | 1 Online-Ressource (xiv, 303 Seiten) |
| ISBN: | 9781009160865 |
| DOI: | 10.1017/9781009160865 |
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| 520 | 3 | |a Starting from where a first course in convex optimization leaves off, this text presents a unified analysis of first-order optimization methods – including parallel-distributed algorithms – through the abstraction of monotone operators. With the increased computational power and availability of big data over the past decade, applied disciplines have demanded that larger and larger optimization problems be solved. This text covers the first-order convex optimization methods that are uniquely effective at solving these large-scale optimization problems. Readers will have the opportunity to construct and analyze many well-known classical and modern algorithms using monotone operators, and walk away with a solid understanding of the diverse optimization algorithms. Graduate students and researchers in mathematical optimization, operations research, electrical engineering, statistics, and computer science will appreciate this concise introduction to the theory of convex optimization algorithms. | |
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| author | Ryu, Ernest K. Yin, Wotao ca. 20./21. Jh |
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| discipline | Mathematik |
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| id | DE-604.BV048683373 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T19:51:58Z |
| institution | BVB |
| isbn | 9781009160865 |
| language | English |
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| publishDate | 2023 |
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| publisher | Cambridge University Press |
| record_format | marc |
| spellingShingle | Ryu, Ernest K. Yin, Wotao ca. 20./21. Jh Large-scale convex optimization algorithms & analyses via monotone operators Monotoner Operator (DE-588)4207161-6 gnd Optimierung (DE-588)4043664-0 gnd Konvexe Menge (DE-588)4165212-5 gnd Operatortheorie (DE-588)4075665-8 gnd Modellierung (DE-588)4170297-9 gnd Konvexe Funktion (DE-588)4139679-0 gnd |
| subject_GND | (DE-588)4207161-6 (DE-588)4043664-0 (DE-588)4165212-5 (DE-588)4075665-8 (DE-588)4170297-9 (DE-588)4139679-0 |
| title | Large-scale convex optimization algorithms & analyses via monotone operators |
| title_auth | Large-scale convex optimization algorithms & analyses via monotone operators |
| title_exact_search | Large-scale convex optimization algorithms & analyses via monotone operators |
| title_full | Large-scale convex optimization algorithms & analyses via monotone operators Ernest K. Ryu (Seoul National University), Wotao Yin (University of California, Los Angeles, and DAMO Academy, Alibaba Group) |
| title_fullStr | Large-scale convex optimization algorithms & analyses via monotone operators Ernest K. Ryu (Seoul National University), Wotao Yin (University of California, Los Angeles, and DAMO Academy, Alibaba Group) |
| title_full_unstemmed | Large-scale convex optimization algorithms & analyses via monotone operators Ernest K. Ryu (Seoul National University), Wotao Yin (University of California, Los Angeles, and DAMO Academy, Alibaba Group) |
| title_short | Large-scale convex optimization |
| title_sort | large scale convex optimization algorithms analyses via monotone operators |
| title_sub | algorithms & analyses via monotone operators |
| topic | Monotoner Operator (DE-588)4207161-6 gnd Optimierung (DE-588)4043664-0 gnd Konvexe Menge (DE-588)4165212-5 gnd Operatortheorie (DE-588)4075665-8 gnd Modellierung (DE-588)4170297-9 gnd Konvexe Funktion (DE-588)4139679-0 gnd |
| topic_facet | Monotoner Operator Optimierung Konvexe Menge Operatortheorie Modellierung Konvexe Funktion |
| url | https://doi.org/10.1017/9781009160865 |
| work_keys_str_mv | AT ryuernestk largescaleconvexoptimizationalgorithmsanalysesviamonotoneoperators AT yinwotao largescaleconvexoptimizationalgorithmsanalysesviamonotoneoperators |