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Accelerated optimization for machine...

Lin, Zhouchen.

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Accelerated optimization for machine learning = first-order algorithms /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Accelerated optimization for machine learning/ by Zhouchen Lin, Huan Li, Cong Fang.
Reminder of title:	first-order algorithms /
Author:	Lin, Zhouchen.
other author:	Li, Huan.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	xxiv, 275 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Introduction -- Chapter 2. Accelerated Algorithms for Unconstrained Convex Optimization -- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization -- Chapter 4. Accelerated Algorithms for Nonconvex Optimization -- Chapter 5. Accelerated Stochastic Algorithms -- Chapter 6. Accelerated Paralleling Algorithms -- Chapter 7. Conclusions.
Contained By:	Springer eBooks
Subject:	Machine learning - Mathematics. -
Online resource:	https://doi.org/10.1007/978-981-15-2910-8
ISBN:	9789811529108

Accelerated optimization for machine learning = first-order algorithms /
Lin, Zhouchen.

Accelerated optimization for machine learningfirst-order algorithms /[electronic resource] :by Zhouchen Lin, Huan Li, Cong Fang. - Singapore :Springer Singapore :2020. - xxiv, 275 p. :ill., digital ;24 cm.

Chapter 1. Introduction -- Chapter 2. Accelerated Algorithms for Unconstrained Convex Optimization -- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization -- Chapter 4. Accelerated Algorithms for Nonconvex Optimization -- Chapter 5. Accelerated Stochastic Algorithms -- Chapter 6. Accelerated Paralleling Algorithms -- Chapter 7. Conclusions.

This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.

ISBN: 9789811529108

Standard No.: 10.1007/978-981-15-2910-8doiSubjects--Topical Terms:

3442737
Machine learning
--Mathematics.

LC Class. No.: Q325.5 / .L569 2020

Dewey Class. No.: 006.310151

Accelerated optimization for machine learning = first-order algorithms /
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505 0 $a Chapter 1. Introduction -- Chapter 2. Accelerated Algorithms for Unconstrained Convex Optimization -- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization -- Chapter 4. Accelerated Algorithms for Nonconvex Optimization -- Chapter 5. Accelerated Stochastic Algorithms -- Chapter 6. Accelerated Paralleling Algorithms -- Chapter 7. Conclusions.
520 $a This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.
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