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Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

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arxiv 1705.07261 v1 pith:Q6BIYYIS submitted 2017-05-20 stat.ML cs.LGmath.OC

Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

Lam M. Nguyen , Jie Liu , Katya Scheinberg , Martin Tak\'a\v{c} This is my paper
classification stat.ML cs.LGmath.OC
keywords gradientnonconvexstochasticrecursivealgorithmconvergencefunctionslosses
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In this paper, we study and analyze the mini-batch version of StochAstic Recursive grAdient algoritHm (SARAH), a method employing the stochastic recursive gradient, for solving empirical loss minimization for the case of nonconvex losses. We provide a sublinear convergence rate (to stationary points) for general nonconvex functions and a linear convergence rate for gradient dominated functions, both of which have some advantages compared to other modern stochastic gradient algorithms for nonconvex losses.

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Cited by 3 Pith papers

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    OptMuon combines orthogonalized momentum with trajectory-dependent AdaGrad-Norm adaptation to obtain expected-stationarity rates of order T^{-1/2} + sigma^{1/2}T^{-1/4} or T^{-1/2} + sigma^{1/3}T^{-1/3} that reduce to...

  3. Accelerating Mini-batch SARAH by Step Size Rules

    cs.LG 2019-06 unverdicted novelty 4.0

    MB-SARAH-RBB uses a random Barzilai-Borwein step size to accelerate mini-batch SARAH, with a linear convergence proof and improved complexity for strongly convex objectives.