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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.

MSC:
90C26 Nonconvex programming, global optimization
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