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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 \(V\) -representation s. 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.

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Parallel Redundancy Removal in lrslib with Application to Projections

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • David Avis,
  • Charles Jordan

摘要

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 \(V\) -representation s. 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.