Name
Title | ||
|---|---|---|
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization |
Abstract | ||
|---|---|---|
We propose a novel adaptive, accelerated algorithm for the stochastic constrained convex optimization setting. Our method, which is inspired by the Mirror-Prox method, simultaneously achieves the optimal rates for smooth/non-smooth problems with either deterministic/stochastic first-order oracles. This is done without any prior knowledge of the smoothness nor the noise properties of the problem. To the best of our knowledge, this is the first adaptive, unified algorithm that achieves the optimal rates in the constrained setting. We demonstrate the practical performance of our framework through extensive numerical experiments. |
Year | Venue | Keywords |
|---|---|---|
2019 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019) | adaptive algorithm,constrained optimization,adaptive algorithms |
Field | DocType | Volume |
Mathematical optimization,Computer science,Adaptive algorithm,Constrained optimization | Conference | 32 |
ISSN | Citations | PageRank |
1049-5258 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Kavis | 1 | 0 | 2.37 |
| Kfir Y. Levy | 2 | 72 | 8.77 |
| Francis Bach | 3 | 11490 | 622.29 |
| Volkan Cevher | 4 | 1860 | 141.56 |