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Fast convergence of stochastic subgradient method under interpolation
We prove that SSGD converges, respectively, with rates $O(1/\epsilon)$ and $O(\log(1/\epsilon))$ for convex and strongly-convex objectives when interpolation holds.
Satisfying Real-world Goals with Dataset Constraints
The goal of minimizing misclassification error on a training set is often just one of several real-world goals that might be defined on different datasets.
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection
There has been significant recent work on the theory and application of randomized coordinate descent algorithms, beginning with the work of Nesterov [SIAM J.
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