Deterministic and stochastic primal-dual subgradient algorithms for uniformly convex minimization
We discuss non-Euclidean deterministic and stochastic algorithms for optimization problems with strongly and uniformly convex objectives. We provide accuracy bounds for the performance of these algorithms and design methods which are adaptive with respect to the parameters of strong or uniform conve...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute for Operations Research and the Management Sciences (INFORMS)
2014-09-01
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| Series: | Stochastic Systems |
| Subjects: | |
| Online Access: | http://www.i-journals.org/ssy/viewarticle.php?id=10&layout=abstract |