14,442 research outputs found

    Solving hard cut problems via flow-augmentation

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    We present a new technique for designing FPT algorithms for graph cut problems in undirected graphs, which we call flow augmentation. Our technique is applicable to problems that can be phrased as a search for an (edge) (s,t)(s,t)-cut of cardinality at most kk in an undirected graph GG with designated terminals ss and tt. More precisely, we consider problems where an (unknown) solution is a set ZE(G)Z \subseteq E(G) of size at most kk such that (1) in GZG-Z, ss and tt are in distinct connected components, (2) every edge of ZZ connects two distinct connected components of GZG-Z, and (3) if we define the set Zs,tZZ_{s,t} \subseteq Z as these edges eZe \in Z for which there exists an (s,t)(s,t)-path PeP_e with E(Pe)Z={e}E(P_e) \cap Z = \{e\}, then Zs,tZ_{s,t} separates ss from tt. We prove that in this scenario one can in randomized time kO(1)(V(G)+E(G))k^{O(1)} (|V(G)|+|E(G)|) add a number of edges to the graph so that with 2O(klogk)2^{-O(k \log k)} probability no added edge connects two components of GZG-Z and Zs,tZ_{s,t} becomes a minimum cut between ss and tt. We apply our method to obtain a randomized FPT algorithm for a notorious "hard nut" graph cut problem we call Coupled Min-Cut. This problem emerges out of the study of FPT algorithms for Min CSP problems, and was unamenable to other techniques for parameterized algorithms in graph cut problems, such as Randomized Contractions, Treewidth Reduction or Shadow Removal. To demonstrate the power of the approach, we consider more generally Min SAT(Γ\Gamma), parameterized by the solution cost. We show that every problem Min SAT(Γ\Gamma) is either (1) FPT, (2) W[1]-hard, or (3) able to express the soft constraint (uv)(u \to v), and thereby also the min-cut problem in directed graphs. All the W[1]-hard cases were known or immediate, and the main new result is an FPT algorithm for a generalization of Coupled Min-Cut

    Approximating subset kk-connectivity problems

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    A subset TVT \subseteq V of terminals is kk-connected to a root ss in a directed/undirected graph JJ if JJ has kk internally-disjoint vsvs-paths for every vTv \in T; TT is kk-connected in JJ if TT is kk-connected to every sTs \in T. We consider the {\sf Subset kk-Connectivity Augmentation} problem: given a graph G=(V,E)G=(V,E) with edge/node-costs, node subset TVT \subseteq V, and a subgraph J=(V,EJ)J=(V,E_J) of GG such that TT is kk-connected in JJ, find a minimum-cost augmenting edge-set FEEJF \subseteq E \setminus E_J such that TT is (k+1)(k+1)-connected in JFJ \cup F. The problem admits trivial ratio O(T2)O(|T|^2). We consider the case T>k|T|>k and prove that for directed/undirected graphs and edge/node-costs, a ρ\rho-approximation for {\sf Rooted Subset kk-Connectivity Augmentation} implies the following ratios for {\sf Subset kk-Connectivity Augmentation}: (i) b(ρ+k)+(3TTk)2H(3TTk)b(\rho+k) + {(\frac{3|T|}{|T|-k})}^2 H(\frac{3|T|}{|T|-k}); (ii) ρO(TTklogk)\rho \cdot O(\frac{|T|}{|T|-k} \log k), where b=1 for undirected graphs and b=2 for directed graphs, and H(k)H(k) is the kkth harmonic number. The best known values of ρ\rho on undirected graphs are min{T,O(k)}\min\{|T|,O(k)\} for edge-costs and min{T,O(klogT)}\min\{|T|,O(k \log |T|)\} for node-costs; for directed graphs ρ=T\rho=|T| for both versions. Our results imply that unless k=To(T)k=|T|-o(|T|), {\sf Subset kk-Connectivity Augmentation} admits the same ratios as the best known ones for the rooted version. This improves the ratios in \cite{N-focs,L}

    Approximating Minimum-Cost k-Node Connected Subgraphs via Independence-Free Graphs

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    We present a 6-approximation algorithm for the minimum-cost kk-node connected spanning subgraph problem, assuming that the number of nodes is at least k3(k1)+kk^3(k-1)+k. We apply a combinatorial preprocessing, based on the Frank-Tardos algorithm for kk-outconnectivity, to transform any input into an instance such that the iterative rounding method gives a 2-approximation guarantee. This is the first constant-factor approximation algorithm even in the asymptotic setting of the problem, that is, the restriction to instances where the number of nodes is lower bounded by a function of kk.Comment: 20 pages, 1 figure, 28 reference
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