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Primal-dual Subgradient Methods for Minimizing Uniformly Convex Functions by Iouditski, Anatoli; Nesterov, Yuri is a scholarly article available to read on EtoBox.

What is Primal-dual Subgradient Methods for Minimizing Uniformly Convex Functions about?

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 convexity of the objective: in the case when the total number of iterations $N$ is fixed, their accuracy coincides, up to a logarithmic in $N$ factor with the accuracy of optimal algorithms.

Author

Iouditski, Anatoli; Nesterov, Yuri

Published

2014

Language

EN