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

被引：0

作者：

João Carlos O. Souza

Paulo Roberto Oliveira

Antoine Soubeyran

机构：

[1] Federal University of Rio de Janeiro,COPPE

[2] Federal University of Piauí,PESC

[3] Aix-Marseille University (Aix-Marseille School of Economics),CEAD

[4] CNRS and EHESS,undefined

来源：

Optimization Letters | 2016年 / 10卷

关键词：

Global optimization; Proximal linearized algorithm ; DC functions; Rate of convergence;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：1529 / 1539

页数：10

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