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arXiv 1805.04071cs.DScs.DC
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有界亏格网络中直径与最小割计算的能效复杂度

The Energy Complexity of Diameter and Minimum Cut Computation in Bounded-Genus Networks

Yi-Jun Chang

AI总结:

本文研究多跳无线电网络中分布式图问题的能效复杂度,针对有界亏格图提出计算精确直径、精确全局最小割及近似 $s$-$t$ 最小割的算法,能耗降至 $\tilde{O}(\sqrt{n})$,核心方法是通过划分顶点度数并利用图结构特性控制连通分量数量。

摘要:

本文研究了多跳无线电网络中分布式图问题的能效复杂度,其中算法的能耗以顶点最大唤醒轮数衡量。近期研究表明,广播、广度优先搜索和极大匹配等问题可用仅消耗 $\text{poly} \log n$ 能效的算法解决。然而,计算图的直径等问题需要 $Ω(n)$ 能效。为提升此类问题的能效,本文聚焦有界亏格图。针对有界亏格图,我们提出了计算精确直径、精确全局最小割大小及 $(1 \pmε)$-近似 $s$-$t$ 最小割大小的算法,其能耗为 $\tilde{O}(\sqrt{n})$。该方法基于通用框架,将顶点集划分为高度数与低度数部分,并利用有界亏格图的结构特性,控制低度数部分诱导子图中特定连通分量的数量。

英文摘要:

This paper investigates the energy complexity of distributed graph problems in multi-hop radio networks, where the energy cost of an algorithm is measured by the maximum number of awake rounds of a vertex. Recent works revealed that some problems, such as broadcast, breadth-first search, and maximal matching, can be solved with energy-efficient algorithms that consume only $\text{poly} \log n$ energy. However, there exist some problems, such as computing the diameter of the graph, that require $Ω(n)$ energy to solve. To improve energy efficiency for these problems, we focus on a special graph class: bounded-genus graphs. We present algorithms for computing the exact diameter, the exact global minimum cut size, and a $(1 \pmε)$-approximate $s$-$t$ minimum cut size with $\tilde{O}(\sqrt{n})$ energy for bounded-genus graphs. Our approach is based on a generic framework that divides the vertex set into high-degree and low-degree parts and leverages the structural properties of bounded-genus graphs to control the number of certain connected components in the subgraph induced by the low-degree part.

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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