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中图分类法:
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O174.13 版次： |
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著者:
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Ryu, Ernest K., |
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题名:
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Large-scale convex optimization : [ algorithms & analyses via monotone operators /] / , |
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出版发行:
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出版地： Cambridge : 出版社： Cambridge University Press, 出版日期： 2023. |
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载体形态:
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xiv, 303 pages : illustrations ; 26 cm |
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内容提要:
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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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主题词:
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Convex sets. |
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主题词:
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Convex functions. |
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主题词:
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Mathematical optimization. |
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主要责任者:
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Yin, Wotao, Yin, Wotao, |