Global convergence of a proximal linearized algorithm for difference of convex functions

A proximal linearized algorithm for minimizing difference of two convex functions is proposed. If the sequence generated by the algorithm is bounded it is proved that every cluster point is a critical point of the function under consideration, even if the auxiliary minimizations are performed inexac...

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Bibliographic Details

Published in:	Optimization letters Vol. 10; no. 7; pp. 1529 - 1539
Main Authors:	Souza, João Carlos O., Oliveira, Paulo Roberto, Soubeyran, Antoine
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2016 Springer Verlag
Subjects:	Computational Intelligence Economics and Finance Humanities and Social Sciences Mathematics Mathematics and Statistics Numerical and Computational Physics Operations Research/Decision Theory Optimization Original Paper Simulation Proximal linearized algorithm Global optimization Rate of convergence DC functions Economie quantitative
ISSN:	1862-4472, 1862-4480
Online Access:	Get full text
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