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In-place algorithms for computing a largest clique in geometric intersection graphs

机译:用于计算几何相交图中最大团的就地算法

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In this paper, we study the problem of designing in-place algorithms for finding the maximum clique in the intersection graphs of axis-parallel rectangles and disks in R~2. First, we propose an O(n~2 log n) time in-place algorithm for finding the maximum clique of the intersection graph of a set of n axis-parallel rectangles of arbitrary sizes. For the intersection graph of fixed height rectangles, the time complexity can be slightly improved to O(n log n+nK), where K is the size of the maximum clique. For disk graphs, we consider two variations of the maximum clique problem, namely geometric clique and graphical clique. The time complexity of our algorithm for finding the largest geometric clique is O(mlog n + n~2) where m is the number of edges in the disk graph, and it works for disks of arbitrary radii. For graphical clique, our proposed algorithm works for unit disks (i.e., of same radii) and the worst case time complexity is O(n~2 + m(n + K~3)); m is the number of edges in the unit disk intersection graph and K is the size of the largest clique in that graph. It uses O(n~3) time in-place computation of maximum matching in a bipartite graph, where the vertices are given in an array, and the existence of an edge between a pair of vertices can be checked by an oracle on demand (from problem specification) in O(1) time. This problem is of independent interest. All these algorithms need O(1) work space in addition to the input array.
机译:在本文中,我们研究了设计就地算法以在R〜2的轴平行矩形和圆盘的交点图中找到最大团的问题。首先,我们提出一种O(n〜2 log n)时间就地算法,以找到任意大小的一组n个轴平行矩形的相交图的最大集团。对于固定高度矩形的相交图,时间复杂度可以稍微提高到O(n log n + nK),其中K是最大团的大小。对于磁盘图,我们考虑最大集团问题的两个变体,即几何集团和图形集团。我们用于查找最大几何团的算法的时间复杂度为O(mlog n + n〜2),其中m是圆盘图中边的数量,它适用于任意半径的圆盘。对于图形集团,我们提出的算法适用于单位圆盘(即相同半径),最坏情况下的时间复杂度为O(n〜2 + m(n + K〜3)); m是单位圆盘相交图中的边数,K是该图中最大团的大小。它使用二分图中最大匹配的O(n〜3)时间就地计算,其中顶点以数组形式给出,并且一对顶点之间的边的存在可以通过oracle来按需检查(从问题规范)在O(1)时间。这个问题是独立利益的。所有这些算法除了输入数组外,还需要O(1)工作空间。

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