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

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HAL-SHS : économie et finance

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info:eu-repo/semantics/altIdentifier/doi/10.1007/s11590-015-0969-1

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Centre pour la communication scientifique directe

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Economie quantitative

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Functions, Convex

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João Carlos O. Souza et al., « Global convergence of a proximal linearized algorithm for difference of convex functions », HAL-SHS : économie et finance, ID : 10.1007/s11590-015-0969-1

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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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