Abstract
We propose a structure-adaptive variant of a state-of-the-art stochastic variance-reduced 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.
Original language | English |
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Pages (from-to) | 429-440 |
Number of pages | 12 |
Journal | Advances in Neural Information Processing Systems |
Publication status | Published - 1 Jan 2018 |
Event | 32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada Duration: 2 Dec 2018 → 8 Dec 2018 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing