4,460 research outputs found

    Max flow vitality in general and stst-planar graphs

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    The \emph{vitality} of an arc/node of a graph with respect to the maximum flow between two fixed nodes ss and tt is defined as the reduction of the maximum flow caused by the removal of that arc/node. In this paper we address the issue of determining the vitality of arcs and/or nodes for the maximum flow problem. We show how to compute the vitality of all arcs in a general undirected graph by solving only 2(n1)2(n-1) max flow instances and, In stst-planar graphs (directed or undirected) we show how to compute the vitality of all arcs and all nodes in O(n)O(n) worst-case time. Moreover, after determining the vitality of arcs and/or nodes, and given a planar embedding of the graph, we can determine the vitality of a `contiguous' set of arcs/nodes in time proportional to the size of the set.Comment: 12 pages, 3 figure

    A Unifying Model of Genome Evolution Under Parsimony

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    We present a data structure called a history graph that offers a practical basis for the analysis of genome evolution. It conceptually simplifies the study of parsimonious evolutionary histories by representing both substitutions and double cut and join (DCJ) rearrangements in the presence of duplications. The problem of constructing parsimonious history graphs thus subsumes related maximum parsimony problems in the fields of phylogenetic reconstruction and genome rearrangement. We show that tractable functions can be used to define upper and lower bounds on the minimum number of substitutions and DCJ rearrangements needed to explain any history graph. These bounds become tight for a special type of unambiguous history graph called an ancestral variation graph (AVG), which constrains in its combinatorial structure the number of operations required. We finally demonstrate that for a given history graph GG, a finite set of AVGs describe all parsimonious interpretations of GG, and this set can be explored with a few sampling moves.Comment: 52 pages, 24 figure

    An Efficient Algorithm for Computing Network Reliability in Small Treewidth

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    We consider the classic problem of Network Reliability. A network is given together with a source vertex, one or more target vertices, and probabilities assigned to each of the edges. Each edge appears in the network with its associated probability and the problem is to determine the probability of having at least one source-to-target path. This problem is known to be NP-hard. We present a linear-time fixed-parameter algorithm based on a parameter called treewidth, which is a measure of tree-likeness of graphs. Network Reliability was already known to be solvable in polynomial time for bounded treewidth, but there were no concrete algorithms and the known methods used complicated structures and were not easy to implement. We provide a significantly simpler and more intuitive algorithm that is much easier to implement. We also report on an implementation of our algorithm and establish the applicability of our approach by providing experimental results on the graphs of subway and transit systems of several major cities, such as London and Tokyo. To the best of our knowledge, this is the first exact algorithm for Network Reliability that can scale to handle real-world instances of the problem.Comment: 14 page

    Connectivity Oracles for Graphs Subject to Vertex Failures

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    We introduce new data structures for answering connectivity queries in graphs subject to batched vertex failures. A deterministic structure processes a batch of ddd\leq d_{\star} failed vertices in O~(d3)\tilde{O}(d^3) time and thereafter answers connectivity queries in O(d)O(d) time. It occupies space O(dmlogn)O(d_{\star} m\log n). We develop a randomized Monte Carlo version of our data structure with update time O~(d2)\tilde{O}(d^2), query time O(d)O(d), and space O~(m)\tilde{O}(m) for any failure bound dnd\le n. This is the first connectivity oracle for general graphs that can efficiently deal with an unbounded number of vertex failures. We also develop a more efficient Monte Carlo edge-failure connectivity oracle. Using space O(nlog2n)O(n\log^2 n), dd edge failures are processed in O(dlogdloglogn)O(d\log d\log\log n) time and thereafter, connectivity queries are answered in O(loglogn)O(\log\log n) time, which are correct w.h.p. Our data structures are based on a new decomposition theorem for an undirected graph G=(V,E)G=(V,E), which is of independent interest. It states that for any terminal set UVU\subseteq V we can remove a set BB of U/(s2)|U|/(s-2) vertices such that the remaining graph contains a Steiner forest for UBU-B with maximum degree ss

    Flow trees for vertex-capacitated networks

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    AbstractGiven a graph G=(V,E) with a cost function c(S)⩾0∀S⊆V, we want to represent all possible min-cut values between pairs of vertices i and j. We consider also the special case with an additive cost c where there are vertex capacities c(v)⩾0 ∀v∈V, and for a subset S⊆V, c(S)=∑v∈Sc(v). We consider two variants of cuts: in the first one, separation, {i} and {j} are feasible cuts that disconnect i and j. In the second variant, vertex-cut, a cut-set that disconnects i from j does not include i or j. We consider both variants for undirected and directed graphs. We prove that there is a flow-tree for separations in undirected graphs. We also show that a compact representation does not exist for vertex-cuts in undirected graphs, even with additive costs. For directed graphs, a compact representation of the cut-values does not exist even with additive costs, for neither the separation nor the vertex-cut cases

    Tight Bounds for Gomory-Hu-like Cut Counting

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    By a classical result of Gomory and Hu (1961), in every edge-weighted graph G=(V,E,w)G=(V,E,w), the minimum stst-cut values, when ranging over all s,tVs,t\in V, take at most V1|V|-1 distinct values. That is, these (V2)\binom{|V|}{2} instances exhibit redundancy factor Ω(V)\Omega(|V|). They further showed how to construct from GG a tree (V,E,w)(V,E',w') that stores all minimum stst-cut values. Motivated by this result, we obtain tight bounds for the redundancy factor of several generalizations of the minimum stst-cut problem. 1. Group-Cut: Consider the minimum (A,B)(A,B)-cut, ranging over all subsets A,BVA,B\subseteq V of given sizes A=α|A|=\alpha and B=β|B|=\beta. The redundancy factor is Ωα,β(V)\Omega_{\alpha,\beta}(|V|). 2. Multiway-Cut: Consider the minimum cut separating every two vertices of SVS\subseteq V, ranging over all subsets of a given size S=k|S|=k. The redundancy factor is Ωk(V)\Omega_{k}(|V|). 3. Multicut: Consider the minimum cut separating every demand-pair in DV×VD\subseteq V\times V, ranging over collections of D=k|D|=k demand pairs. The redundancy factor is Ωk(Vk)\Omega_{k}(|V|^k). This result is a bit surprising, as the redundancy factor is much larger than in the first two problems. A natural application of these bounds is to construct small data structures that stores all relevant cut values, like the Gomory-Hu tree. We initiate this direction by giving some upper and lower bounds.Comment: This version contains additional references to previous work (which have some overlap with our results), see Bibliographic Update 1.

    Traffic-Redundancy Aware Network Design

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    We consider network design problems for information networks where routers can replicate data but cannot alter it. This functionality allows the network to eliminate data-redundancy in traffic, thereby saving on routing costs. We consider two problems within this framework and design approximation algorithms. The first problem we study is the traffic-redundancy aware network design (RAND) problem. We are given a weighted graph over a single server and many clients. The server owns a number of different data packets and each client desires a subset of the packets; the client demand sets form a laminar set system. Our goal is to connect every client to the source via a single path, such that the collective cost of the resulting network is minimized. Here the transportation cost over an edge is its weight times times the number of distinct packets that it carries. The second problem is a facility location problem that we call RAFL. Here the goal is to find an assignment from clients to facilities such that the total cost of routing packets from the facilities to clients (along unshared paths), plus the total cost of "producing" one copy of each desired packet at each facility is minimized. We present a constant factor approximation for the RAFL and an O(log P) approximation for RAND, where P is the total number of distinct packets. We remark that P is always at most the number of different demand sets desired or the number of clients, and is generally much smaller.Comment: 17 pages. To be published in the proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithm

    Parameterized Algorithms for Zero Extension and Metric Labelling Problems

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    We consider the problems Zero Extension and Metric Labelling under the paradigm of parameterized complexity. These are natural, well-studied problems with important applications, but have previously not received much attention from this area. Depending on the chosen cost function mu, we find that different algorithmic approaches can be applied to design FPT-algorithms: for arbitrary mu we parameterize by the number of edges that cross the cut (not the cost) and show how to solve Zero Extension in time O(|D|^{O(k^2)} n^4 log n) using randomized contractions. We improve this running time with respect to both parameter and input size to O(|D|^{O(k)} m) in the case where mu is a metric. We further show that the problem admits a polynomial sparsifier, that is, a kernel of size O(k^{|D|+1}) that is independent of the metric mu. With the stronger condition that mu is described by the distances of leaves in a tree, we parameterize by a gap parameter (q - p) between the cost of a true solution q and a `discrete relaxation\u27 p and achieve a running time of O(|D|^{q-p} |T|m + |T|phi(n,m)) where T is the size of the tree over which mu is defined and phi(n,m) is the running time of a max-flow computation. We achieve a similar result for the more general Metric Labelling, while also allowing mu to be the distance metric between an arbitrary subset of nodes in a tree using tools from the theory of VCSPs. We expect the methods used in the latter result to have further applications
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