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

João Carlos O. Souza (1) , Paulo Roberto Oliveira (1) , Antoine Soubeyran (2)

1 Federal University of Rio de Janeiro
2 AMSE - Aix-Marseille Sciences Economiques

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.

Mots clés: Economie quantitative

Domaines: Sciences de l'Homme et Société; Economies et finances

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⟩