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Finding a Center Tree of Phylogenetic Trees via Leaf Removal

机译:通过叶子去除来寻找系统发育树的中心树

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Given a set T = {T1, T2, . . . , Tm} of phylogenetic trees with the same leaf-label set X, we wish to remove some leaves from the trees so that there is a tree T with leaf-label set X displaying all the resulting trees. Note that different leaves may be removed from different input trees. One objective is to minimize the total number of leaves removed from the trees, while the other is to minimize the maximum number of leaves removed from an input tree. Chauve et al. [2] refer to the problem with the first (respectively, second) objective as AST-LR (respectively, AST-LR-d), and show that both problems are NP-hard. They further present algorithms for the parameterized versions of both problems. In this paper, we point out that their algorithm for the parameterized version of AST-LR is flawed. We then design integer-linear programming (ILP) models for AST-LR and ASTLR-d, and discuss speedup issues when using popular ILP solvers (say, GUROBI or CPLEX) to solve the models. Our experimental results show that our ILP approach is relatively efficient.
机译:给定一个设置t = {t 1 ,T. 2 。 。 。 ,T. m 用相同的叶标签组X的系统发育树,我们希望从树上删除一些叶子,以便有一棵树t,叶标签集x显示所有得到的树木。注意,可以从不同的输入树中移除不同的叶子。一个目标是最小化从树上移除的叶子的总数,而另一个是最小化从输入树中移除的最大叶子数。 Chauve等人。 [2]请参阅第一个(分别,第二)目标作为AST-LR(分别,AST-LR-D)的问题,并表明这两个问题都是NP-HARD。它们进一步存在于两个问题的参数化版本的算法。在本文中,我们指出了它们的参数化版本的AST-LR算法缺陷。然后,我们为AST-LR和ASTLR-D设计整数线性编程(ILP)模型,并在使用流行的ILP求解器(例如,GUROBI或CPLEX)来解决模型时讨论加速问题。我们的实验结果表明,我们的ILP方法相对较高。

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