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Accelerated optimization for machine...
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Fang, Cong.
Accelerated optimization for machine learningfirst-order algorithms /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Accelerated optimization for machine learningby 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.
Contained By:
Springer eBooks
Subject:
Machine learningMathematics.
Online resource:
https://doi.org/10.1007/978-981-15-2910-8
ISBN:
9789811529108$q(electronic bk.)
Accelerated optimization for machine learningfirst-order algorithms /
Lin, Zhouchen.
Accelerated optimization for machine learning
first-order algorithms /[electronic resource] :by Zhouchen Lin, Huan Li, Cong Fang. - Singapore :Springer Singapore :2020. - xxiv, 275 p. :ill., digital ;24 cm.
Chapter 1. Introduction -- Chapter 2. Accelerated Algorithms for Unconstrained Convex Optimization -- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization -- Chapter 4. Accelerated Algorithms for Nonconvex Optimization -- Chapter 5. Accelerated Stochastic Algorithms -- Chapter 6. Accelerated Paralleling Algorithms -- Chapter 7. Conclusions.
This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.
ISBN: 9789811529108$q(electronic bk.)
Standard No.: 10.1007/978-981-15-2910-8doiSubjects--Topical Terms:
857106
Machine learning
--Mathematics.
LC Class. No.: Q325.5 / .L569 2020
Dewey Class. No.: 006.310151
Accelerated optimization for machine learningfirst-order algorithms /
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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.
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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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EB Q325.5 .L735 2020 2020
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https://doi.org/10.1007/978-981-15-2910-8
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