A Computation-Efficient Decentralized Algorithm for Composite Constrained Optimization

This paper focuses on solving the problem of composite constrained convex optimization with a sum of smooth convex functions and non-smooth regularization terms (ℓ 1 norm) subject to locally general constraints. Motivated by the modern large-scale information processing problems in machine learning...

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Veröffentlicht in:	IEEE transactions on signal and information processing over networks Jg. 6; S. 774 - 789
Hauptverfasser:	Lu, Qingguo, Liao, Xiaofeng, Li, Huaqing, Huang, Tingwen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Composite constrained optimization computation-efficient Computational efficiency Computational geometry Constituents Constraints Convergence Convex analysis Convex functions Convexity Data processing decentralized stochastic algorithm differential privacy Gradient methods Information processing Linear programming Machine learning Optimization Privacy Regularization Task analysis Upper bounds variance reduction
ISSN:	2373-776X, 2373-7778
Online-Zugang:	Volltext
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