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 ...
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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.
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Add this copy of Modern Nonconvex Nondifferentiable Optimization to cart. $147.31, new condition, Sold by GreatBookPrices rated 4.0 out of 5 stars, ships from Columbia, MD, UNITED STATES, published 2021 by Society for Industrial and Applied Mathematics (SIAM).
Edition:
2021, Society for Industrial and Applied Mathematics (SIAM)
Publisher:
Society for Industrial and Applied Mathematics (SIAM)
Published:
2021
Language:
English
Alibris ID:
18360272381
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Edition:
2021, Society for Industrial and Applied Mathematics (SIAM)
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2021, Society for Industrial and Applied Mathematics (SIAM)
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Edition:
2021, Society for Industrial and Applied Mathematics (SIAM)
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Edition:
2021, Society for Industrial and Applied Mathematics (SIAM)
