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First-Order Methods in Optimization

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Amir Beck 作者
SIAM-Society for Industrial and Applied Mathematics
譯者
2017-10-2 出版日期
494 頁數
0 價格
MOS-SIAM Series on Optimization 叢書系列
9781611974980 圖書編碼

First-Order Methods in Optimization 在線電子書 圖書標籤: 優化  數學  Optimization  Math.Optimization  Math   


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First-Order Methods in Optimization 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

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First-Order Methods in Optimization 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



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First-Order Methods in Optimization 在線電子書 著者簡介

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.


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First-Order Methods in Optimization 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

First-Order Methods in Optimization 在線電子書 圖書描述

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 offers comprehensive study of first-order methods with the theoretical foundations; provides plentiful examples and illustrations; emphasizes rates of convergence and complexity analysis of the main first-order methods used to solve large-scale problems; and covers both variables and functional decomposition methods.

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.

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