SVRG meets SAGA: k-SVRG --- A Tale of Limited Memory
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In recent years, many variance reduced algorithms for empirical risk minimization have been introduced. In contrast to vanilla SGD, these methods converge linearly on strong convex problems. To obtain the variance reduction, current methods either require frequent passes over the full data to recompute gradients---without making any progress during this time (like in SVRG), or they require memory of the same size as the input problem (like SAGA). In this work, we propose k-SVRG, an algorithm that interpolates between those two extremes: it makes best use of the available memory and in turn does avoid full passes over the data without making progress. We prove linear convergence of k-SVRG on strongly convex problems and convergence to stationary points on non-convex problems. Numerical experiments show the effectiveness of our method.
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