Convergence rate analysis of proximal gradient methods with applications to composite minimization problems

First-order methods such as proximal gradient, which use Forward-Backward Splitting techniques have proved to be very effective in solving nonsmooth convex minimization problem, which is useful in solving various practical problems in different fields such as machine learning and image processing. I...

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Bibliographic Details

Published in:	Optimization Vol. 70; no. 1; pp. 75 - 100
Main Authors:	Sahu, D. R., Yao, J. C., Verma, M., Shukla, K. K.
Format:	Journal Article
Language:	English
Published:	Philadelphia Taylor & Francis 02.01.2021 Taylor & Francis LLC
Subjects:	Accretive operator Algorithms Convergence convex minimization problem Datasets forward-backward splitting algorithm Image processing Machine learning Mann iteration method Optimization Regression analysis S-iteration method Splitting
ISSN:	0233-1934, 1029-4945
Online Access:	Get full text
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