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Approximation and Exact Algorithms for Construction MInimum Ultrametric Trees from Distance Matrices

机译:从距离矩阵构造最小树的逼近和精确算法

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摘要

An edge-weighted tree is called ultrametric if the distances from the root to all the leaves in the tree are equal. For an n by n distance matrix M, the minimum ultrametric tree for M is an ultrametric tree T=(V, E, w )with leaf set {1, ..., n }such that d-t(i, j )>= M[i, j]for all i, j and sum _(the point e belong to (is member of ) the set E) #omega#(e)is minimum, where d_T(i, j)is the distance between i and j on T. Constructing minimum ultrametric trees from distance matrices is an important problem in computational biology. In this paper, we examine its computational complexity and approximability . When the distances satisfy the triangle inequality, we show that the minimum ultrametric tree problem can be approximated in polynomial time with error ratio 1.5(1+log n),where n is the number of species. We also develop an efficient branch-and-bound algorithm for constructing the minimum ultrametric tree for both metric and non-metric inputs. The experimental results show that it can find an optimal solution for 25 species within reasonable time, while, to the best of our knowledge, there is no report of algorithms solving the problem even for 12 species.
机译:如果从树根到所有叶子的距离相等,则边缘加权树称为超度量树。对于n x n距离矩阵M,M的最小超树是具有叶子集{1,...,n}使得dt(i,j)> =的超树T =(V,E,w)对于所有i,j和和_(点e属于集合E的成员()的成员)M [i,j]#omega#(e)最小,其中d_T(i,j)是i之间的距离从距离矩阵构造最小超测树是计算生物学中的一个重要问题。在本文中,我们检查了其计算复杂度和可逼近性。当距离满足三角形不等式时,我们证明最小超树问题可以在多项式时间内近似,误差比为1.5(1 + log n),其中n是物种数。我们还开发了一种有效的分支定界算法,用于为度量和非度量输入构建最小超度量树。实验结果表明,该算法可以在合理的时间内找到25种物种的最优解,而据我们所知,甚至没有算法可以解决12种物种的问题。

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