Accelerated Variance Reduced Block Coordinate Descent
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Algorithms with fast convergence, small number of data access, and low per-iteration complexity are particularly favorable in the big data era, due to the demand for obtaining highly accurate solutions to problems with a large number of samples in ultra-high dimensional space. Existing algorithms lack at least one of these qualities, and thus are inefficient in handling such big data challenge. In this paper, we propose a method enjoying all these merits with an accelerated convergence rate O(1/k^2). Empirical studies on large scale datasets with more than one million features are conducted to show the effectiveness of our methods in practice.
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