Redundancies in Linear Systems with two Variables per Inequality

TL;DR

This paper introduces a strongly polynomial algorithm for detecting redundancies in linear systems where each inequality involves at most two variables, improving efficiency over previous methods.

Contribution

The paper develops a new strongly polynomial algorithm for redundancy detection in LI(2) systems, combining Clarkson's method with Hochbaum and Naor's technique.

Findings

Algorithm runs in O(n d^2 s log s) time

Redundancy detection is feasible with the same complexity as feasibility testing

Enhanced efficiency over previous algorithms for LI(2) systems

Abstract

The problem of detecting and removing redundant constraints is fundamental in optimization. We focus on the case of linear programs (LPs), given by $d$ variables with $n$ inequality constraints. A constraint is called \emph{redundant}, if after its removal, the LP still has the same feasible region. The currently fastest method to detect all redundancies is due to Clarkson: it solves $n$ linear programs, but each of them has at most $s$ constraints, where $s$ is the number of nonredundant constraints. In this paper, we study the special case where every constraint has at most two variables with nonzero coefficients. This family, denoted by $LI(2)$, has some nice properties. Namely, as shown by Aspvall and Shiloach, given a variable $x_i$ and a value $\lambda$, we can test in time $O(nd)$ whether there is a feasible solution with $x_i = \lambda$. Hochbaum and Naor present an $O(d^2 n \log n)$ algorithm for solving the feasibility problem in $LI(2)$. Their technique makes use of the Fourier-Motzkin elimination method and the earlier mentioned result by Aspvall and Shiloach. We present a strongly polynomial algorithm that solves redundancy detection in time $O(n d^2 s \log s)$. It uses a modification of Clarkson's algorithm, together with a revised version of Hochbaum and Naor's technique. Finally we show that dimensionality testing can be done with the same running time as solving feasibility.