Linearly convergent stochastic heavy ball method for minimizing generalization error

Nicolas Loizou; Peter Richtárik

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arXiv · 1710.10737

Linearly convergent stochastic heavy ball method for minimizing generalization error

Nicolas Loizou·Peter Richtárik

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Abstract

In this work we establish the first linear convergence result for the stochastic heavy ball method. The method performs SGD steps with a fixed stepsize, amended by a heavy ball momentum term. In the analysis, we focus on minimizing the expected loss and not on finite-sum minimization, which is typically a much harder problem. While in the analysis we constrain ourselves to quadratic loss, the overall objective is not necessarily strongly convex.

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Nicolas Loizou, Peter Richtárik. 2017-10-30. Linearly convergent stochastic heavy ball method for minimizing generalization error. https://arxiv.org/abs/1710.10737

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