- Additional Authors
- Yin, Wotao.
- Description
- 1 online resource (xiv, 303 pages) : digital, PDF file(s).
- Summary
- 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.
- Uniform Title
- Large-scale convex optimization (Online)
- Alternative Title
- Large-scale convex optimization (Online)
- Subject
- Note
- Title from publisher's bibliographic system (viewed on 12 Dec 2022).
- Access (note)
- Access restricted to authorized users.
- OCLC
- ssj0002829127
- Author
Ryu, Ernest K.
- Title
Large-scale convex optimization [electronic resource] : algorithms & analyses via monotone operators / Ernest K. Ryu, Wotao Yin.
- Imprint
Cambridge : Cambridge University Press, 2023.
- Access
Access restricted to authorized users.
- Connect to:
- Added Author
Yin, Wotao.
- Other Form:
Print version: 9781009160858