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arXiv 0908.1448cs.DScs.DM
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快速生成随机生成树

Faster generation of random spanning trees

Jonathan A. Kelner, Aleksander Madry

摘要:

本文提出了一种在无向图中生成近似一致随机生成树的新算法。我们展示了如何从一个在预期时间$\TO(m\sqrt{n}\log 1/δ)$均匀的乘法$(1+δ)$内的分布中采样。这改善了稀疏图的情况下,最好的以前已知的最坏情况下的界的$O(\min \{mn, n^{2.376}\})$,这已经站了二十年。为了实现这一目标,我们利用图和电网络上的随机游走之间的连接,我们用这个引入一个新的方法来解决这个问题,集成离散随机游走为基础的技术与连续线性代数方法。我们相信,我们使用的电气网络和稀疏线性系统解算器与随机游走和组合分区技术是一个有用的范例,将找到进一步的应用在算法图论。

英文摘要:

In this paper, we set forth a new algorithm for generating approximately uniformly random spanning trees in undirected graphs. We show how to sample from a distribution that is within a multiplicative $(1+δ)$ of uniform in expected time $\TO(m\sqrt{n}\log 1/δ)$. This improves the sparse graph case of the best previously known worst-case bound of $O(\min \{mn, n^{2.376}\})$, which has stood for twenty years. To achieve this goal, we exploit the connection between random walks on graphs and electrical networks, and we use this to introduce a new approach to the problem that integrates discrete random walk-based techniques with continuous linear algebraic methods. We believe that our use of electrical networks and sparse linear system solvers in conjunction with random walks and combinatorial partitioning techniques is a useful paradigm that will find further applications in algorithmic graph theory.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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