Abstract
Consider the stochastic composition optimization problem where the objective is a composition of two expected-value functions. We propose a new stochastic firstorder method, namely the accelerated stochastic compositional proximal gradient (ASC-PG) method, which updates based on queries to the sampling oracle using two different timescales. The ASC-PG is the first proximal gradient method for the stochastic composition problem that can deal with nonsmooth regularization penalty. We show that the ASC-PG exhibits faster convergence than the best known algorithms, and that it achieves the optimal sample-error complexity in several important special cases. We further demonstrate the application of ASC-PG to reinforcement learning and conduct numerical experiments.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1722-1730 |
| Number of pages | 9 |
| Journal | Advances in Neural Information Processing Systems |
| State | Published - 2016 |
| Externally published | Yes |
| Event | 30th Annual Conference on Neural Information Processing Systems, NIPS 2016 - Barcelona, Spain Duration: Dec 5 2016 → Dec 10 2016 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing