Finding minimum-quotient cuts in planar graphs

James K. Park, C. Phillips
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引用次数: 53

Abstract

Given a graph G = (V, E) where each vertex v {element_of} V is assigned a weight w(v) and each edge e {element_of} E is assigned a cost c(e), the quotient of a cut partitioning the vertices of V into sets S and {bar S} is c(S, {bar S})/min{l_brace}w(S), w(S){r_brace}, where c(S, {bar S}) is the sum of the costs of the edges crossing the cut and w(S) and w({bar S}) are the sum of the weights of the vertices in S and {bar S}, respectively. The problem of finding a cut whose quotient is minimum for a graph has in recent years attracted considerable attention, due in large part to the work of Rao and Leighton and Rao. They have shown that an algorithm (exact or approximation) for the minimum-quotient-cut problem can be used to obtain an approximation algorithm for the more famous minimumb-balanced-cut problem, which requires finding a cut (S,{bar S}) minimizing c(S,{bar S}) subject to the constraint bW {le} w(S) {le} (1 {minus} b)W, where W is the total vertex weight and b is some fixed balance in the range 0 < b {le} {1/2}. Unfortunately, the minimum-quotient-cut problem is strongly NP-hard for general graphs, and themore » best polynomial-time approximation algorithm known for the general problem guarantees only a cut whose quotient is at mostO(lg n) times optimal, where n is the size of the graph. However, for planar graphs, the minimum-quotient-cut problem appears more tractable, as Rao has developed several efficient approximation algorithms for the planar version of the problem capable of finding a cut whose quotient is at most some constant times optimal. In this paper, we improve Rao`s algorithms, both in terms of accuracy and speed. As our first result, we present two pseudopolynomial-time exact algorithms for the planar minimum-quotient-cut problem. As Rao`s most accurate approximation algorithm for the problem -- also a pseudopolynomial-time algorithm -- guarantees only a 1.5-times-optimal cut, our algorithms represent a significant advance.« less
在平面图中求最小商切
给定一个图G = (V, E),每个顶点V {element_of}被赋予一个重量w (V)和每条边E {element_of} E分配成本c (E)的商分区的顶点V切成集年代和年代}{酒吧是c (S, S}{酒吧)/分钟{l_brace} w (S), w (S) {r_brace},其中c (S,{酒吧年代})是成本之和的边缘穿越削减和w (S)和w({酒吧年代})顶点的权值之和年代和年代}{酒吧,分别。近年来,寻找图中商最小的切的问题引起了相当大的关注,这在很大程度上要归功于Rao、Leighton和Rao的工作。他们已经证明,最小商切问题的算法(精确或近似)可以用来获得更著名的最小平衡切问题的近似算法,该问题需要找到一个cut (S,{bar S})最小化c(S,{bar S}),服从约束bW {le} w(S) {le} (1 {-} b) w,其中w是总顶点权值,b是范围0 < b{{1/2}内的某个固定平衡。不幸的是,对于一般图来说,最小商切问题是强np困难的,而对于一般问题,已知的最好的多项式时间近似算法只能保证商最多为to (lgn)倍最优的切,其中n是图的大小。然而,对于平面图形,最小商切问题似乎更容易处理,因为Rao已经开发了几种有效的近似算法来解决平面版本的问题,这些算法能够找到商最多是某个常数倍最优的切。在本文中,我们从精度和速度两个方面改进了Rao的算法。作为我们的第一个结果,我们给出了平面最小商切问题的两个伪多项式时间精确算法。由于Rao对该问题最精确的近似算法(也是伪多项式时间算法)只能保证1.5倍的最优切割,因此我们的算法代表了一个重大的进步。«少
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