Faster Exact Computation of rSPR Distance via Better Approximation

Chen, Zhizhong; Harada, Youta; Nakamura, Yuna; Wang, Lusheng

doi:10.1109/tcbb.2018.2878731

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…The problem of computing the rSPR distance of two trees is NP-hard (Hein et al, 1996;Bordewich and Semple, 2005). This has motivated researchers to design approximation algorithms for the problem (Schalekamp et al, 2016;Chen et al, 2017) as well as fixedparameter algorithms for the problem (Whidden et al, 2010;Chen et al, 2015Chen et al, , 2017.…”

Section: Introductionmentioning

confidence: 99%

Computing a Consensus Phylogeny via Leaf Removal

Chen

Ueta

et al. 2020

Journal of Computational Biology

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Given a set T = fT 1 ‚ T 2 ‚. .. ‚ T m g. 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 the labels of leaves removed from one input tree may be different from those of leaves removed from another input tree. One objective is to minimize the total number of leaves removed from the trees, whereas the other is to minimize the maximum number of leaves removed from an input tree. Chauve et al. refer to the problem with the first (respectively, second) objective as AST-LR (respectively, AST-LR-d), and they show that both problems are NP-hard, where NP is the class of problems solvable in non-deterministic polynomial time. They further present algorithms for the parameterized versions of both problems. In this article, we point out that their algorithm for the parameterized version of AST-LR is flawed and present a new algorithm. Since neither Chauve et al.'s algorithm for AST-LR-d nor our new algorithm for AST-LR looks practical, we further design integer-linear programming (ILP for short) models for AST-LR and AST-LR-d, and we 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 quite efficient.

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