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

João Carlos O. Souza Paulo Roberto Oliveira Antoine Soubeyran 1
1 GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille
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.
Keywords : Economie quantitative
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Journal articles
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Submitted on : Thursday, January 19, 2017 - 11:15:02 AM
Last modification on : Tuesday, February 8, 2022 - 10:53:45 AM

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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, Springer Verlag, 2016, 10 (7), pp.1529--1539. ⟨10.1007/s11590-015-0969-1⟩. ⟨hal-01440298⟩

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