Stochastic proximal quasi-Newton methods for non-convex composite optimization
Sign Up to like & getrecommendations! Published in 2019 at "Optimization Methods and Software"
DOI: 10.1080/10556788.2018.1471141
Abstract: ABSTRACT In this paper, we propose a generic algorithmic framework for stochastic proximal quasi-Newton (SPQN) methods to solve non-convex composite optimization problems. Stochastic second-order information is explored to construct proximal subproblem. Under mild conditions we… read more here.
Keywords: stochastic proximal; proximal quasi; quasi newton; optimization ... See more keywords
Snake: A Stochastic Proximal Gradient Algorithm for Regularized Problems Over Large Graphs
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Automatic Control"
DOI: 10.1109/tac.2019.2890888
Abstract: A regularized optimization problem over a large unstructured graph is studied, where the regularization term is tied to the graph geometry. Typical regularization examples include the total variation and the Laplacian regularizations over the graph.… read more here.
Keywords: regularized problems; proximal gradient; gradient algorithm; algorithm ... See more keywords