Efficient numerical algorithms for regularized regression problem with applications to traffic matrix estimations
Optimization and Control
2016-04-19 v9
Abstract
In this work we collect and compare to each other many different numerical methods for regularized regression problem and for the problem of projection on a hyperplane. Such problems arise, for example, as a subproblem of demand matrix estimation in IP- networks. In this special case matrix of affine constraints has special structure: all elements are 0 or 1 and this matrix is sparse enough. We have to deal with huge-scale convex optimization problem of special type. Using the properties of the problem we try "to look inside the black-box" and to see how the best modern methods work being applied to this problem.
Cite
@article{arxiv.1508.00858,
title = {Efficient numerical algorithms for regularized regression problem with applications to traffic matrix estimations},
author = {Anton Anikin and Pavel Dvurechensky and Alexander Gasnikov and Andrey Golov and Alexander Gornov and Yury Maximov and Mikhail Mendel and Vladimir Spokoiny},
journal= {arXiv preprint arXiv:1508.00858},
year = {2016}
}
Comments
16 pages; Information Technologies and Systems. Sochi: September, 2015