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Journal Articles Optimization Letters Year : 2016

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

Abstract

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.

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Dates and versions

hal-01440298 , version 1 (13-02-2023)

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João Carlos O. Souza, Paulo Roberto Oliveira, Antoine Soubeyran. Global convergence of a proximal linearized algorithm for difference of convex functions. Optimization Letters, 2016, 10 (7), pp.1529--1539. ⟨10.1007/s11590-015-0969-1⟩. ⟨hal-01440298⟩
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