Efficient numerical algorithms for regularized regression problem with applications to traffic matrix estimations
Year: 2015
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Anikin, Anton, Dvurechensky, Pavel, Gasnikov, Alexander, Golov, Andrey, Gornov, Alexander, Maximov, Yury, Mendel, Mikhail, Spokoiny, Vladimir
Language: English
Topic: Matrix Theory and Algorithms
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2412196465