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We present a stochastic first-order optimization algorithm, named BCSC, thatadds a cyclic constraint to stochastic block-coordinate descent. It usesdifferent subsets of the data to update different subsets of the parameters,thus limiting the detrimental effect of outliers in the training set. Empiricaltests in benchmark datasets show that our algorithm outperformsstate-of-the-art optimization methods in both accuracy as well as convergencespeed. The improvements are consistent across different architectures, and canbe combined with other training techniques and regularization methods.

Block-Cyclic Stochastic Coordinate Descent for Deep Neural Networks