Modern Nonconvex Nondifferentiable Optimization
Will be published approx. on 28. February 2022
Book
Hardback
774 pages
978-1-61197-673-1 (ISBN)
Description
Starting with the fundamentals of classical smooth optimization and building on established convex programming techniques, this research monograph presents a foundation and methodology for modern nonconvex nondifferentiable optimization. It provides readers with theory, methods, and applications of nonconvex and nondifferentiable optimization in statistical estimation, operations research, machine learning, and decision making.
A comprehensive and rigorous treatment of this emergent mathematical topic is urgently needed in today's complex world of big data and machine learning. This book takes a thorough approach to the subject and includes examples and exercises to enrich the main themes, making it suitable for classroom instruction.
Modern Nonconvex Nondifferentiable Optimization is intended for applied and computational mathematicians, optimizers, operations researchers, statisticians, computer scientists, engineers, economists, and machine learners. It could be used in advanced courses on optimization/operations research and nonconvex and nonsmooth optimization.
A comprehensive and rigorous treatment of this emergent mathematical topic is urgently needed in today's complex world of big data and machine learning. This book takes a thorough approach to the subject and includes examples and exercises to enrich the main themes, making it suitable for classroom instruction.
Modern Nonconvex Nondifferentiable Optimization is intended for applied and computational mathematicians, optimizers, operations researchers, statisticians, computer scientists, engineers, economists, and machine learners. It could be used in advanced courses on optimization/operations research and nonconvex and nonsmooth optimization.
More details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Weight
1752 gr
ISBN-13
978-1-61197-673-1 (9781611976731)
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Schweitzer Classification
Persons
Ying Cui is an Assistant Professor of Industrial and Systems Engineering at the University of Minnesota. Previously, she spent over two years as a postdoctoral associate at the University of Southern California. Her research focuses on the mathematical foundation of data science with emphasis on optimization techniques for operations research, machine learning, and statistical estimations.
Jong-Shi Pang is the Epstein Family Chair and Professor of Industrial and Systems Engineering at the University of Southern California. Since July 2019, he has served as the Editor-in-Chief of the SIAM Journal on Optimization. His research interests include mathematical modeling and analysis of a wide range of complex engineering and economics systems, with a focus in operations research, single and multi-agent optimization, equilibrium programming, and constrained dynamical systems. In February 2021, he became a member of the National Academy of Engineering.
Jong-Shi Pang is the Epstein Family Chair and Professor of Industrial and Systems Engineering at the University of Southern California. Since July 2019, he has served as the Editor-in-Chief of the SIAM Journal on Optimization. His research interests include mathematical modeling and analysis of a wide range of complex engineering and economics systems, with a focus in operations research, single and multi-agent optimization, equilibrium programming, and constrained dynamical systems. In February 2021, he became a member of the National Academy of Engineering.