Convex Optimization Algorithms 1st Edition by Dimitri P. Bertsekas (PDF)

Description

This book, developed through class instruction at MIT over the last 15 years, provides an accessible, concise, and intuitive presentation of algorithms for solving convex optimization problems. It relies on rigorous mathematical analysis, but also aims at an intuitive exposition that makes use of visualization where possible. This is facilitated by the extensive use of analytical and algorithmic concepts of duality, which by nature lend themselves to geometrical interpretation. The book places particular emphasis on modern developments, and their widespread applications in fields such as large-scale resource allocation problems, signal processing, and machine learning. Among its features, the book:* Develops comprehensively the theory of descent and approximation methods, including gradient and subgradient projection methods, cutting plane and simplicial decomposition methods, and proximal methods* Describes and analyzes augmented Lagrangian methods, and alternating direction methods of multipliers* Develops the modern theory of coordinate descent methods, including distributed asynchronous convergence analysis* Comprehensively covers incremental gradient, subgradient, proximal, and constraint projection methods* Includes optimal algorithms based on extrapolation techniques, and associated rate of convergence analysis* Describes a broad variety of applications of large-scale optimization and machine learning* Contains many examples, illustrations, and exercises* Is structured to be used conveniently either as a standalone text for a class on convex analysis and optimization, or as a theoretical supplement to either an applications/convex optimization models class or a nonlinear programming class

User’s Reviews

Editorial Reviews: Review Throughout the book, the writing style is very clear, compact and easy to follow, but at the same time mathematically rigorous. … The book contains a fair number of exercises, many of them supplementing the algorithmic development and analysis. All the algorithms presented in different chapters are clearly explained and the important implementation considerations are discussed. … This book can be recommended as a valuable material for both self study and teaching purposes, but because of its rigorous style it works also as a valuable reference for research purposes. — Review by A. Kumar Neogy –Zentralblatt MATH 1347, October 2017 About the Author Dimitri Bertsekas is McAffee Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, and a member of the National Academy of Engineering. He has researched a broad variety of subjects from optimization theory, control theory, parallel and distributed computation, systems analysis, and data communication networks. He has written numerous papers in each of these areas, and he has authored or coauthored sixteen textbooks. Professor Bertsekas was awarded the INFORMS 1997 Prize for Research Excellence in the Interface Between Operations Research and Computer Science for his book “Neuro-Dynamic Programming” (co-authored with John Tsitsiklis), the 2001 ACC John R. Ragazzini Education Award, the 2009 INFORMS Expository Writing Award, the 2014 ACC Richard E. Bellman Control Heritage Award for “contributions to the foundations of deterministic and stochastic optimization-based methods in systems and control,” the 2014 Khachiyan Prize for Life-Time Accomplishments in Optimization, and the 2015 George B. Dantzig Prize. In 2018, he was awarded jointly with John Tsitsiklis, the INFORMS John von Neumann Theory Prize, for the contributions of the research monographs “Parallel and Distributed Computation” and “Neuro-Dynamic Programming”. In 2001, he was elected to the United States National Academy of Engineering for “pioneering contributions to fundamental research, practice and education of optimization/control theory.”

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Unlike many optimization books which just throw a bunch of obscure mathematical proofs, this book discusses many state-of-the-art examples in each chapter, which are very helpful in understanding the theories. Excellent!

⭐A good comprehensive graduate level summary of convex optimization algorithms with ample applications!

⭐That’s an excellent book with the state-of-the-art mathematics in optimization.

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