On the Computational Complexity of the Rooted Subtree Prune and Regraft Distance

The graph-theoretic operation of rooted subtree prune and regraft is increasingly being used as a tool for understanding and modelling reticulation events in evolutionary biology. In this paper, we show that computing the rooted subtree prune and regraft distance between two rooted binary phylogenetic trees on the same label set is NP-hard. This resolves a longstanding open problem. Furthermore, we show that this distance is fixed parameter tractable when parameterised by the distance between the two trees.Received March 16, 2004     

A Cluster Reduction for Computing the Subtree Distance Between Phylogenies

Calculating the rooted subtree prune and regraft (rSPR) distance between two rooted binary phylogenetic trees is a frequently applied process in various areas of molecular evolution. However, computing this distance is an NP-hard problem and practical algorithms for computing it exactly are rare. In this paper, a divide-and-conquer approach to calculating the rSPR distance is established. This approach breaks the problem instance into a number of smaller and more tractable subproblems. Two reduction rules which were previously used to show that computing the rSPR distance is fixed-parameter tractable can easily be used to complement this new theoretical result, and so a significant positive impact on the running time of calculating this distance in practice is likely.     

A note on the relaxation time of two Markov chains on rooted phylogenetic tree spaces

Phylogenetic trees are commonly used to model the evolutionary relationships among a collection of biological species. Over the past fifteen years, the convergence properties for Markov chains defined on phylogenetic trees have been studied, yielding results about the time required for such chains to converge to their stationary distributions. In this work we derive an upper bound on the relaxation time of two Markov chains on rooted binary trees: one defined by nearest neighbor interchanges (NNI) and the other defined by subtree prune and regraft (SPR) moves.     

On agreement forests

Tree rearrangement operations are widely used to measure the dissimilarity between phylogenetic trees with identical leaf sets. The tree bisection and reconnection (tbr) distance for unrooted trees can be equivalently defined in terms of agreement forests. For both the tbr distance and the less general subtree prune and regraft (spr) distance, we use such forests to derive new upper and lower bounds on the maximal possible distance between two trees with n leaves.     

Exploiting locality: approximating sorting buffers

The sorting buffers problem is motivated by many applications in manufacturing processes and computer science, among them car-painting and file servers architecture. The input is a sequence of items of various types. All the items must be processed, one by one, by a service station. We are given a random-access sorting buffer with a limited capacity. Whenever a new item arrives it may be moved directly to the service station or stored in the buffer. Also, at any time items can be removed from the buffer and assigned to the service station. Our goal is to give the service station a sequence of items with minimum type transitions. We generalize the problem to allow items with different sizes and type transitions with different costs. We give a polynomial-time 9-approximation algorithm for the maximization variant of this problem, which improves the best previously known 20-approximation algorithm.     

When a worse approximation factor gives better performance: a 3-approximation algorithm for the vertex k-center problem

The vertex k-center selection problem is a well known NP-Hard minimization problem that can not be solved in polynomial time within a \(\rho < 2\) approximation factor, unless \(P=NP\). Even though there are algorithms that achieve this 2-approximation bound, they perform poorly on most benchmarks compared to some heuristic algorithms. This seems to happen because the 2-approximation algorithms take, at every step, very conservative decisions in order to keep the approximation guarantee. In this paper we propose an algorithm that exploits the same structural properties of the problem that the 2-approximation algorithms use, but in a more relaxed manner. Instead of taking the decision that guarantees a 2-approximation, our algorithm takes the best decision near the one that guarantees the 2-approximation. This results in an algorithm with a worse approximation factor (a 3-approximation), but that outperforms all the previously known approximation algorithms on the most well known benchmarks for the problem, namely, the pmed instances from OR-Lib (Beasly in J Oper Res Soc 41(11):1069–1072, 1990) and some instances from TSP-Lib (Reinelt in ORSA J Comput 3:376–384, 1991). However, the \(O(n^4)\) running time of this algorithm becomes unpractical as the input grows. In order to improve its running time, we modified this algorithm obtaining a \(O(n^2 \log n)\) heuristic that outperforms not only all the previously known approximation algorithms, but all the polynomial heuristics proposed up to date.     

On the monotonicity of a data stream

In this paper we consider problems related to the sortedness of a data stream. In the first part of this work, we investigate the problem of estimating the distance to monotonicity; given a finite stream of length n from alphabet {1,...,m}, we give a deterministic (2+?)-approximation algorithm for estimating its distance to monotonicity in space \(O\left( {\tfrac{1} {{\varepsilon ^2 }}\log ^2 (\varepsilon n)} \right)\). This improves over the previous randomized (4+?)-approximation algorithm due to Gopalan, Jayram, Krauthgamer and Kumar in SODA 2007.     

Finding long and similar parts of trajectories

A natural time-dependent similarity measure for two trajectories is their average distance at corresponding times. We give algorithms for computing the most similar subtrajectories under this measure, assuming the two trajectories are given as two polygonal, possibly self-intersecting lines with time stamps. For the case when a minimum duration of the subtrajectories is specified and the subtrajectories must start at corresponding times, we give a linear-time algorithm. The algorithm is based on a result of independent interest: We present a linear-time algorithm to find, for a piece-wise monotone function, an interval of at least a given length that has minimum average value. In the case that the subtrajectories may start at non-corresponding times, it appears difficult to give exact algorithms, even if the duration of the subtrajectories is fixed. For this case, we give (1+ε)-approximation algorithms, for both fixed duration and when only a minimum duration is specified.     

顾客为子树结构的树上反中心选址问题

顾客为子树结构的树上反中心选址问题是在树T上寻找一点(位于顶点处或在边的内部),使得该点与子树结构的顾客之间的最小赋权带加数距离尽可能地大.给出了该问题的一个有效算法,其时间复杂度为O(cn+sum from j=1 to m n_j),其中n_j为各子树T_j的顶点个数,c为不同的子树权重个数,n为树的顶点数.     

A Linear Time Algorithm for Finding ak-Tree Core

Given a tree containingnvertices, consider the sum of the distance between all vertices and ak-leaf subtree (subtree which contains exactlykleaves). Ak-tree core is ak-leaf subtree which minimizes the sum of the distances. In this paper, we propose a linear time algorithm for finding ak-tree core for a givenk.     

An Algorithmic Framework for the Exact Solution of the Prize-Collecting Steiner Tree Problem

The Prize-Collecting Steiner Tree Problem (PCST) on a graph with edge costs and vertex profits asks for a subtree minimizing the sum of the total cost of all edges in the subtree plus the total profit of all vertices not contained in the subtree. PCST appears frequently in the design of utility networks where profit generating customers and the network connecting them have to be chosen in the most profitable way. Our main contribution is the formulation and implementation of a branch-and-cut algorithm based on a directed graph model where we combine several state-of-the-art methods previously used for the Steiner tree problem. Our method outperforms the previously published results on the standard benchmark set of problems. We can solve all benchmark instances from the literature to optimality, including some of them for which the optimum was not known. Compared to a recent algorithm by Lucena and Resende, our new method is faster by more than two orders of magnitude. We also introduce a new class of more challenging instances and present computational results for them. Finally, for a set of large-scale real-world instances arising in the design of fiber optic networks, we also obtain optimal solution values. Received: April, 2004 This work has been partly supported by the RTNADONET, 504438, by the Doctoral Scholarship Program of the Austrian Academy of Sciences (DOC) and by CNR and MIUR, Italy.A preliminary version of this paper appeared as [21].     

Approximation algorithms for the single allocation problem in hub-and-spoke networks and related metric labeling problems

This paper deals with a single allocation problem in hub-and-spoke networks. We present a simple deterministic 3-approximation algorithm and randomized 2-approximation algorithm based on a linear relaxation problem and a randomized rounding procedure. We handle the case where the number of hubs is three, which is known to be NP-hard, and present a (5/4)-approximation algorithm.The single allocation problem includes a special class of the metric labeling problem, defined by introducing an assumption that both objects and labels are embedded in a common metric space. Under this assumption, we can apply our algorithms to the metric labeling problem without losing theoretical approximation ratios. As a byproduct, we also obtain a (4/3)-approximation algorithm for an ordinary metric labeling problem with three labels.     

A note on the generalized min-sum set cover problem

In this paper, we consider the generalized min-sum set cover problem, introduced by Azar, Gamzu, and Yin (STOC 2009). Bansal, Gupta, and Krishnaswamy (SODA 2010) give a 485-approximation algorithm for the problem. We are able to alter their algorithm and analysis to obtain a 28-approximation algorithm, improving the performance guarantee by an order of magnitude. We use concepts from α-point scheduling to obtain our improvements.     

Approximation algorithms for <Emphasis Type="Italic">k</Emphasis>-echelon extensions of the one warehouse multi-retailer problem

In this paper, we consider k-echelon extensions of the deterministic one warehouse multi-retailer problem. We give constant factor approximation algorithms for some of these extensions when k is fixed. We focus first on the case without backorders and we give a \((2k-1)\)-approximation algorithm under general assumptions on the evolution of the holding costs as products move toward the final customers. We then improve this result to a k-approximation when the holding costs are monotonically non-increasing or non-decreasing (which is a natural situation in practice). Finally we address problems with backorders: we give a 3-approximation for the one-warehouse multi-retailer problem with backlog and a k-approximation algorithm for the k-level Joint Replenishment Problem with backlog (a variant where inventory can only be kept at the final retailers). Ours results are the first constant approximation algorithms for those problems. In addition, we demonstrate the potential of our approach on a practical case. Our preliminary experiments show that the average optimality gap is around 15%.     

On the average number of nodes in a subtree of a tree

For any tree T (labelled, not rooted) of order n, it will be shown that the average number of nodes in a subtree of T is at least $\text{(n + 2)}\text{3}$, with this minimum achieved iff T is a path. For T rooted, the average number of nodes in a subtree containing the root is at least $\text{(n + 1)}\text{2}$ and always exceeds the average over all unrooted subtrees. For the maximum mean, examples show that there are arbitrarily large trees in which the average subtree contains essentially all of the nodes. The mean subtree order as a function on trees is also shown to be monotone with respect to inclusion.     

Efficient algorithms for wavelength assignment on trees of rings

A fundamental problem in communication networks is wavelength assignment (WA): given a set of routing paths on a network, assign a wavelength to each path such that the paths with the same wavelength are edge-disjoint, using the minimum number of wavelengths. The WA problem is NP-hard for a tree of rings network which is well used in practice. In this paper, we give an efficient algorithm which solves the WA problem on a tree of rings with an arbitrary (node) degree using at most 3L wavelengths and achieves an approximation ratio of 2.75 asymptotically, where L is the maximum number of paths on any link in the network. The 3L upper bound is tight since there are instances of the WA problem that require 3L wavelengths even on a tree of rings with degree four. We also give a 3L and 2-approximation (resp. 2.5-approximation) algorithm for the WA problem on a tree of rings with degree at most six (resp. eight). Previous results include: 4L (resp. 3L) wavelengths for trees of rings with arbitrary degrees (resp. degree at most eight), and 2-approximation (resp. 2.5-approximation) algorithm for trees of rings with degree four (resp. six).     

Constrained global optimization of multivariate polynomials using Bernstein branch and prune algorithm

We propose an algorithm for constrained global optimization to tackle non-convex nonlinear multivariate polynomial programming problems. The proposed Bernstein branch and prune algorithm is based on the Bernstein polynomial approach. We introduce several new features in this proposed algorithm to make the algorithm more efficient. We first present the Bernstein box consistency and Bernstein hull consistency algorithms to prune the search regions. We then give Bernstein contraction algorithm to avoid the computation of Bernstein coefficients after the pruning operation. We also include a new Bernstein cut-off test based on the vertex property of the Bernstein coefficients. The performance of the proposed algorithm is numerically tested on 13 benchmark problems. The results of the tests show the proposed algorithm to be overall considerably superior to existing method in terms of the chosen performance metrics.     

Inverse sequences, rooted trees and their end spaces

In this paper we prove that if we consider the standard real metric on simplicial rooted trees then the category Tower-Set of inverse sequences can be described by means of the bounded coarse geometry of the naturally associated trees. Using this we give a geometrical characterization of Mittag-Leffler property in inverse sequences in terms of the metrically proper homotopy type of the corresponding tree and its maximal geodesically complete subtree. We also obtain some consequences in shape theory. In particular we describe some new representations of shape morphisms related to infinite branches in trees.     

The intersection graphs of subtrees in trees are exactly the chordal graphs

The intersection graph of a family of subtrees in an undirected tree is called a subtree graph. A graph is called chordal if every simple circuit with more than three vertices has an edge connecting two non-consecutive vertices. In this paper, we prove that, for a graph G, the following conditions are equivalent: (i) G is a chordal graph; (ii) G is a subtree graph; (iii) G is a proper subtree graph.Consider a chordal graph G. We give an efficient algorithm for constructing a representation of G by a family of subtrees in a tree.     

A 2 + ɛ approximation algorithm for the k-MST problem

For any ɛ > 0 we give a (2 + ɛ)-approximation algorithm for the problem of finding a minimum tree spanning any k vertices in a graph (k-MST), improving a 3-approximation algorithm by Garg [10]. As in [10] the algorithm extends to a (2 + ɛ)-approximation algorithm for the minimum tour that visits any k vertices, provided the edge costs satisfy the triangle inequality. Research supported by NSF CAREER award NSF CCR-9502747, NSF grants CCR-0205594 and CCR-0098180, an Alfred Sloan Fellowship, and a Packard Fellowship. Research supported by an NSERC Discovery grant.