Variable metric proximal stochastic variance reduced gradient methods for nonconvex nonsmooth optimization

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Variable metric proximal stochastic variance reduced gradient methods for nonconvex nonsmooth optimization

We study the problem of minimizing the sum of two functions. The first function is the average of a large number of nonconvex component functions and the second function is a convex (possibly nonsmooth) function that admits a simple proximal mapping. With a diagonal Barzilai-Borwein stepsize for upd...

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Bibliographic Details
Main Authors:	Yu, T., Liu, X.W., Dai, Y.H., Sun, Jie
Format:	Journal Article
Published:	2022
Online Access:	http://hdl.handle.net/20.500.11937/91423

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