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First-Order Methods in Optimization

2017-10-02 Mathematics
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Release : 2017-10-02
Genre : Mathematics
Kind : eBook
Book Rating : 984/5 ( reviews)

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Book Synopsis First-Order Methods in Optimization by : Amir Beck

Download or read book First-Order Methods in Optimization written by Amir Beck. This book was released on 2017-10-02. Available in PDF, EPUB and Kindle. Book excerpt: The primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-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.

First-Order Methods in Optimization

2017-10-02 Mathematics
Download First-Order Methods in Optimization PDF Online Free

Author :
Release : 2017-10-02
Genre : Mathematics
Kind : eBook
Book Rating : 992/5 ( reviews)

GET EBOOK


Book Synopsis First-Order Methods in Optimization by : Amir Beck

Download or read book First-Order Methods in Optimization written by Amir Beck. This book was released on 2017-10-02. Available in PDF, EPUB and Kindle. Book excerpt: The primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-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.

First-order and Stochastic Optimization Methods for Machine Learning

2020-05-15 Mathematics
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Release : 2020-05-15
Genre : Mathematics
Kind : eBook
Book Rating : 685/5 ( reviews)

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Book Synopsis First-order and Stochastic Optimization Methods for Machine Learning by : Guanghui Lan

Download or read book First-order and Stochastic Optimization Methods for Machine Learning written by Guanghui Lan. This book was released on 2020-05-15. Available in PDF, EPUB and Kindle. Book excerpt: This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.

Introduction to Nonlinear Optimization

2014-10-27 Mathematics
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Release : 2014-10-27
Genre : Mathematics
Kind : eBook
Book Rating : 651/5 ( reviews)

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Book Synopsis Introduction to Nonlinear Optimization by : Amir Beck

Download or read book Introduction to Nonlinear Optimization written by Amir Beck. This book was released on 2014-10-27. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the foundations of the theory of nonlinear optimization as well as some related algorithms and presents a variety of applications from diverse areas of applied sciences. The author combines three pillars of optimization?theoretical and algorithmic foundation, familiarity with various applications, and the ability to apply the theory and algorithms on actual problems?and rigorously and gradually builds the connection between theory, algorithms, applications, and implementation. Readers will find more than 170 theoretical, algorithmic, and numerical exercises that deepen and enhance the reader's understanding of the topics. The author includes offers several subjects not typically found in optimization books?for example, optimality conditions in sparsity-constrained optimization, hidden convexity, and total least squares. The book also offers a large number of applications discussed theoretically and algorithmically, such as circle fitting, Chebyshev center, the Fermat?Weber problem, denoising, clustering, total least squares, and orthogonal regression and theoretical and algorithmic topics demonstrated by the MATLAB? toolbox CVX and a package of m-files that is posted on the book?s web site.

Variational Methods in Optimization

1998-01-01 Mathematics
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Release : 1998-01-01
Genre : Mathematics
Kind : eBook
Book Rating : 554/5 ( reviews)

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Book Synopsis Variational Methods in Optimization by : Donald R. Smith

Download or read book Variational Methods in Optimization written by Donald R. Smith. This book was released on 1998-01-01. Available in PDF, EPUB and Kindle. Book excerpt: Highly readable text elucidates applications of the chain rule of differentiation, integration by parts, parametric curves, line integrals, double integrals, and elementary differential equations. 1974 edition.

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