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

Linearly convergent stochastic heavy ball method for minimizing generalization error