Parallel Redundancy Removal in lrslib with Application to Projections

Abstract

We describe a parallel implementation in lrslib for removing redundant halfspaces and finding a minimum representation for an H-representation of a convex polyhedron. By a standard transformation, the same code works for V-representations. We use this approach to speed up the redundancy removal step in Fourier-Motzkin elimination. Computational results are given including a comparison with Clarkson's algorithm, which is particularly fast on highly redundant inputs.