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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Summary: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 inexactly at each iteration. Linear convergence of the sequence is established under suitable additional assumptions.
ISSN:1862-4472
1862-4480
DOI:10.1007/s11590-015-0969-1