Large-Scale Convex Optimization

Ernest K. Ryu is Assistant Professor of Mathematical Sciences at Seoul National University. He previously served as Assistant Adjunct Professor with the Department of Mathematics at the University of California, Los Angeles from 2016 to 2019, before joining Seoul National University in 2020. He received a BS with distinction in physics and electrical engineering from the California Institute of Technology in 2010; and then an MS in statistics and a PhD – with the Gene Golub Best Thesis Award – in computational mathematics at Stanford University in 2016. His current research focuses on mathematical optimization and machine learning. Wotao Yin is Director of the Decision Intelligence Lab with Alibaba Group (US), Damo Academy, and a former Professor of Mathematics at the University of California, Los Angeles. He received his PhD in operations research from Columbia University in 2006. His numerous accolades include an NSF CAREER Award in 2008, an Alfred P. Sloan Research Fellowship in 2009, a Morningside Gold Medal in 2016, and a Damo Award and Egon Balas Prize in 2021. He invented fast algorithms for sparse optimization, image processing, and large-scale distributed optimization problems, and is among the top 1 percent of cited researchers by Clarivate Analytics. His research interests include computational optimization and its applications in signal processing, machine learning, and other data science problems.

'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

• Preface; 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.