Optimization proxies have the potential to transform various applications through significant improvements in efficiency.
New theoretical and experimental approaches help explain the self-organization of ice under turbulence.
We can trace the evolution of iterative methods up to the present day by examining several pivotal big ideas.
Koopman operator theory has recently emerged as the primary candidate for extracting human-interpretable models from data.
In 2023, SIAM partnered with the Livermore Lab Foundation to support an undergraduate student internship.
Ernest Davis reviews two books about issues of privacy and inequality with facial recognition technologies.
The Hong Kong Polytechnic University SIAM Student Chapter organized an exciting event with experts in the field of optimization.
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2017 / x + 484 pages / Softcover / ISBN 978-1-611974-98-0 / List Price $97.00 / SIAM Member Price $67.90 / Order Code MO25
Keywords: nonlinear optimization; convex analysis; first order methods; decomposition methods ; scientific computing
Contents Preface Index
Chapter 6 Chapter 10
The primary goal of this book is to provide a self-contained, comprehensive study of the main first-order methods that are frequently used in solving large-scale problems. First-order methods exploit information on values and gradients/subgradients (but not Hessians) of the functions composing the model under consideration. With the increase in the number of applications that can be modeled as large or even huge-scale optimization problems, there has been a revived interest in using simple methods that require low iteration cost as well as low memory storage.
The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books.
First-Order Methods in Optimization
Audience This book is intended primarily for researchers and graduate students in mathematics, computer sciences, and electrical and other engineering departments. Readers with a background in advanced calculus and linear algebra, as well as prior knowledge in the fundamentals of optimization (some convex analysis, optimality conditions, and duality), will be best prepared for the material. About the Author Amir Beck is a Professor at the School of Mathematical Sciences, Tel-Aviv University. His research interests are in continuous optimization, including theory, algorithmic analysis, and its applications. He has published numerous papers and has given invited lectures at international conferences. He serves in the editorial board of several journals. His research has been supported by various funding agencies, including the Israel Science Foundation, the German-Israeli Foundation, the United States–Israel Binational Science Foundation, the Israeli Science and Energy ministries and the European Community.
ISBN 9781611974980
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