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

João Carlos O. Souza, Paulo Roberto Oliveira and Antoine Soubeyran
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João Carlos O. Souza: Federal University of Rio de Janeiro
Paulo Roberto Oliveira: Federal University of Rio de Janeiro

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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 (search for similar items in EconPapers)
Date: 2016-10
Note: View the original document on HAL open archive server: https://amu.hal.science/hal-01440298
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Citations: View citations in EconPapers (5)

Published in Optimization Letters, 2016, 10 (7), pp.1529--1539. ⟨10.1007/s11590-015-0969-1⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01440298

DOI: 10.1007/s11590-015-0969-1

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