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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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https://hal-amu.archives-ouvertes.fr/hal-01440298
Contributor : Patrice Cacciuttolo <>
Submitted on : Thursday, January 19, 2017 - 11:15:02 AM
Last modification on : Wednesday, August 5, 2020 - 3:10:13 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, 2016, 10 (7), pp.1529--1539. ⟨10.1007/s11590-015-0969-1⟩. ⟨hal-01440298⟩

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