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A Semismooth Newton Stochastic Proximal Point Algorithm with Variance Reduction
March 27, 2024, 4:45 a.m. | Andre Milzarek, Fabian Schaipp, Michael Ulbrich
stat.ML updates on arXiv.org arxiv.org
Abstract: We develop an implementable stochastic proximal point (SPP) method for a class of weakly convex, composite optimization problems. The proposed stochastic proximal point algorithm incorporates a variance reduction mechanism and the resulting SPP updates are solved using an inexact semismooth Newton framework. We establish detailed convergence results that take the inexactness of the SPP steps into account and that are in accordance with existing convergence guarantees of (proximal) stochastic variance-reduced gradient methods. Numerical experiments show …
abstract algorithm arxiv class framework math.oc optimization stat.ml stochastic type updates variance
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