國立虎尾科技大學 |

Accelerated Optimization for Machine Learning = First-Order Algorithms /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Accelerated Optimization for Machine Learning / by Zhouchen Lin, Huan Li, Cong Fang.
其他題名:	First-Order Algorithms /
作者:	Lin, Zhouchen.
其他作者:	Fang, Cong.
面頁冊數:	XXIV, 275 p. 36 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational Mathematics and Numerical Analysis. -
電子資源:	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 Learning First-Order Algorithms /[electronic resource] :by Zhouchen Lin, Huan Li, Cong Fang. - 1st ed. 2020. - XXIV, 275 p. 36 illus.online resource.

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:

669338
Computational Mathematics and Numerical Analysis.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Accelerated Optimization for Machine Learning = First-Order Algorithms /
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