Efficient numerical algorithms for regularized regression problem with applications to traffic matrix estimations

Anton Anikin; Pavel Dvurechensky; Alexander Gasnikov; Andrey Golov; Alexander Gornov; Yury Maximov; Mikhail Mendel; Vladimir Spokoiny

arXiv · 1508.00858

Efficient numerical algorithms for regularized regression problem with applications to traffic matrix estimations

Anton Anikin·Pavel Dvurechensky·Alexander Gasnikov·Andrey Golov·Alexander Gornov·Yury Maximov·Mikhail Mendel·Vladimir Spokoiny

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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.

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Anton Anikin, Pavel Dvurechensky, Alexander Gasnikov, Andrey Golov, Alexander Gornov, Yury Maximov, Mikhail Mendel, Vladimir Spokoiny. 2015-08-04. Efficient numerical algorithms for regularized regression problem with applications to traffic matrix estimations. https://arxiv.org/abs/1508.00858

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