Accelerated First-Order Optimization Algorithms for Machine Learning

Numerical optimization serves as one of the pillars of machine learning. To meet the demands of big data applications, lots of efforts have been put on designing theoretically and practically fast algorithms. This article provides a comprehensive survey on accelerated first-order algorithms with a f...

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Bibliographic Details
Published in:	Proceedings of the IEEE Vol. 108; no. 11; pp. 2067 - 2082
Main Authors:	Li, Huan, Fang, Cong, Lin, Zhouchen
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Acceleration Algorithms Approximation algorithms Complexity theory Computational geometry Convergence Convex analysis Convex functions convex optimization Convexity deterministic algorithms First order algorithms Machine learning Machine learning algorithms nonconvex optimization Numerical analysis Optimization Optimization methods stochastic algorithms
ISSN:	0018-9219, 1558-2256
Online Access:	Get full text
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