Modern Nonconvex Nondifferentiable Optimization Hardback
by Ying Cui, Jong-Shi Pang
Part of the MOS-SIAM Series on Optimization series
Hardback
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.
Information
-
Out of stock
- Format:Hardback
- Pages:774 pages
- Publisher:Society for Industrial & Applied Mathematics,U.S.
- Publication Date:28/02/2022
- Category:
- ISBN:9781611976731
Information
-
Out of stock
- Format:Hardback
- Pages:774 pages
- Publisher:Society for Industrial & Applied Mathematics,U.S.
- Publication Date:28/02/2022
- Category:
- ISBN:9781611976731
