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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Veröffentlicht in:	Proceedings of the IEEE Jg. 108; H. 11; S. 2067 - 2082
Hauptverfasser:	Li, Huan, Fang, Cong, Lin, Zhouchen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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