Large-Scale Convex Optimization : Algorithm Analysis Via Monotone Operators by Wotao Yin and Ernest K. Ryu (2022, Hardcover)

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About this product

Product Identifiers

PublisherCambridge University Press

ISBN-101009160850

ISBN-139781009160858

eBay Product ID (ePID)7057260032

Product Key Features

Number of Pages400 Pages

Publication NameLarge-Scale Convex Optimization : Algorithm Analysis Via Monotone Operators

LanguageEnglish

SubjectGeneral

Publication Year2022

FeaturesNew Edition

TypeTextbook

Subject AreaMathematics

AuthorWotao Yin, Ernest K. Ryu

FormatHardcover

Dimensions

Item Height0.8 in

Item Length10.3 in

Item Width7.2 in

Additional Product Features

Dewey Edition23

Reviews'Ryu and Yin's Large-Scale Convex Optimization does a great job of covering a field with a long history and much current interest. The book describes dozens of algorithms, from classic ones developed in the 1970s to some very recent ones, in unified and consistent notation, all organized around the basic concept and unifying theme of a monotone operator. I strongly recommend it to any mathematician, researcher, or engineer who uses, or has an interest in, convex optimization.' Stephen Boyd, Stanford University

IllustratedYes

Dewey Decimal516.08

Edition DescriptionNew Edition

Table Of ContentPreface; 1. Introduction and preliminaries; Part I. Monotone Operator Methods: 2. Monotone operators and base splitting schemes; 3. Primal-dual splitting methods; 4. Parallel computing; 5. Randomized coordinate update methods; 6. Asynchronous coordinate update methods; Part II. Additional Topics: 7. Stochastic optimization; 8. ADMM-type methods; 9. Duality in splitting methods; 10. Maximality and monotone operator theory; 11. Distributed and decentralized optimization; 12. Acceleration; 13. Scaled relative graphs; Appendices; References; Index.

SynopsisStarting 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., This introduction to the theory of convex optimization algorithms presents a unified analysis of first-order optimization methods using the abstraction of monotone operators. The text empowers graduate students in mathematics, computer science, and engineering to choose and design the splitting methods best suited for a given problem.

LC Classification NumberQA402.5