The proximal point method revisited∗
نویسنده
چکیده
In this short survey, I revisit the role of the proximal point method in large scale optimization. I focus on three recent examples: a proximally guided subgradient method for weakly convex stochastic approximation, the prox-linear algorithm for minimizing compositions of convex functions and smooth maps, and Catalyst generic acceleration for regularized Empirical Risk Minimization.
منابع مشابه
Graph Convergence for H(.,.)-co-Accretive Mapping with over-Relaxed Proximal Point Method for Solving a Generalized Variational Inclusion Problem
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