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Rescaling algorithms for linear conic feasibility

Dadush, Daniel, Végh, László A. and Zambelli, Giacomo (2020) Rescaling algorithms for linear conic feasibility. Mathematics of Operations Research, 45 (2). 732 - 754. ISSN 0364-765X

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Identification Number: 10.1287/moor.2019.1011

Abstract

We propose simple polynomial-time algorithms for two linear conic feasibility problems. For a matrix A ∈ R m× n, the kernel problem requires a positive vector in the kernel of A, and the image problem requires a positive vector in the image of A T. Both algorithms iterate between simple first-order steps and rescaling steps. These rescalings improve natural geometric potentials. If Goffin's condition measure ρ A is negative, then the kernel problem is feasible, and the worst-case complexity of the kernel algorithm is O((m 3n + mn 2)log|ρ A| −1); if ρ A > 0, then the image problem is feasible, and the image algorithm runs in time O(m 2n 2 log ρ A −1). We also extend the image algorithm to the oracle setting. We address the degenerate case ρA = 0 by extending our algorithms to find maximum support nonnegative vectors in the kernel of A and in the image of A T. In this case, the running time bounds are expressed in the bit-size model of computation: for an input matrix A with integer entries and total encoding length L, the maximum support kernel algorithm runs in time O((m 3n + mn 2)L), whereas the maximum support image algorithm runs in time O(m 2n 2L). The standard linear programming feasibility problem can be easily reduced to either maximum support problems, yielding polynomial-time algorithms for linear programming.

Item Type: Article
Official URL: https://pubsonline.informs.org/journal/moor
Additional Information: © 2020 INFORMS
Divisions: Mathematics
Subjects: Q Science > QA Mathematics
Date Deposited: 13 May 2019 13:24
Last Modified: 28 Jun 2020 23:26
URI: http://eprints.lse.ac.uk/id/eprint/100778

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