A Spectral Approach to Polytope Diameter

IF 0.6 3区 数学 Q4 COMPUTER SCIENCE, THEORY & METHODS Discrete & Computational Geometry Pub Date : 2024-03-16 DOI:10.1007/s00454-024-00636-y
Hariharan Narayanan, Rikhav Shah, Nikhil Srivastava
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Abstract

We prove upper bounds on the graph diameters of polytopes in two settings. The first is a worst-case bound for polytopes defined by integer constraints in terms of the height of the integers and certain subdeterminants of the constraint matrix, which in some cases improves previously known results. The second is a smoothed analysis bound: given an appropriately normalized polytope, we add small Gaussian noise to each constraint. We consider a natural geometric measure on the vertices of the perturbed polytope (corresponding to the mean curvature measure of its polar) and show that with high probability there exists a “giant component” of vertices, with measure \(1-o(1)\) and polynomial diameter. Both bounds rely on spectral gaps—of a certain Schrödinger operator in the first case, and a certain continuous time Markov chain in the second—which arise from the log-concavity of the volume of a simple polytope in terms of its slack variables.

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多面体直径的谱学方法
我们证明了两种情况下多边形图直径的上限。第一种是整数约束定义的多面体的最坏情况约束,即整数高度和约束矩阵的某些子决定因素,这在某些情况下改进了之前已知的结果。第二种是平滑分析约束:给定一个适当归一化的多面体,我们给每个约束添加小的高斯噪声。我们考虑了扰动多面体顶点的自然几何度量(对应于其极点的平均曲率度量),并证明顶点很有可能存在一个 "巨大分量",其度量为(1-o(1)\),直径为多项式。在第一种情况下,这两种约束都依赖于某个薛定谔算子的谱差距;在第二种情况下,依赖于某个连续时间马尔可夫链的谱差距。
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来源期刊
Discrete & Computational Geometry
Discrete & Computational Geometry 数学-计算机:理论方法
CiteScore
1.80
自引率
12.50%
发文量
99
审稿时长
6-12 weeks
期刊介绍: Discrete & Computational Geometry (DCG) is an international journal of mathematics and computer science, covering a broad range of topics in which geometry plays a fundamental role. It publishes papers on such topics as configurations and arrangements, spatial subdivision, packing, covering, and tiling, geometric complexity, polytopes, point location, geometric probability, geometric range searching, combinatorial and computational topology, probabilistic techniques in computational geometry, geometric graphs, geometry of numbers, and motion planning.
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