Primal-dual accelerated gradient methods with small-dimensional relaxation oracle
Abstract
In this paper, a new variant of accelerated gradient descent is proposed. The pro-posed method does not require any information about the objective function, usesexact line search for the practical accelerations of convergence, converges accordingto the well-known lower bounds for both convex and non-convex objective functions,possesses primal-dual properties and can be applied in the non-euclidian set-up. Asfar as we know this is the rst such method possessing all of the above properties atthe same time. We also present a universal version of the method which is applicableto non-smooth problems. We demonstrate how in practice one can efficiently use thecombination of line-search and primal-duality by considering a convex optimizationproblem with a simple structure (for example, linearly constrained).
Cite
@article{arxiv.1809.05895,
title = {Primal-dual accelerated gradient methods with small-dimensional relaxation oracle},
author = {Yurii Nesterov and Alexander Gasnikov and Sergey Guminov and Pavel Dvurechensky},
journal= {arXiv preprint arXiv:1809.05895},
year = {2019}
}