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Global convergence of a proximal linearized algorithm for difference of convex functions. (English) Zbl 1355.90073
Summary: 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.

90C26 Nonconvex programming, global optimization
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