Proximal Algorithms for a class of abstract convex functions
arxiv(2024)
摘要
In this paper we analyze a class of nonconvex optimization problem from the
viewpoint of abstract convexity. Using the respective generalizations of the
subgradient we propose an abstract notion proximal operator and derive a number
of algorithms, namely an abstract proximal point method, an abstract
forward-backward method and an abstract projected subgradient method. Global
convergence results for all algorithms are discussed and numerical examples are
given
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