We propose a structure-adaptive variant of a state-of-the-art stochastic variancereduced gradient algorithm Katyusha for regularized empirical risk minimization. The proposed method is able to exploit the intrinsic low-dimensional structure of the solution, such as sparsity or low rank which is enforced by a non-smooth regularization, to achieve even faster convergence rate. This provable algorithmic improvement is done by restarting the Katyusha algorithm according to restricted strong-convexity (RSC) constants. We also propose an adaptive-restart variant which is able to estimate the RSC on the fly and adjust the restart period automatically. We demonstrate the effectiveness of our approach via numerical experiments.

We propose a structure-adaptive variant of a state-of-the-art stochastic variancereduced gradient algorithm Katyusha for regularized empirical risk minimization. The proposed method is able to exploit the intrinsic low-dimensional structure of the solution, such as sparsity or low rank which is enforced by a non-smooth regularization, to achieve even faster convergence rate. This provable algorithmic improvement is done by restarting the Katyusha algorithm according to restricted strong-convexity (RSC) constants. We also propose an adaptive-restart variant which is able to estimate the RSC on the fly and adjust the restart period automatically. We demonstrate the effectiveness of our approach via numerical experiments.