20 research outputs found

    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 k-Connected m-Dominating Sets

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    A subset SS of nodes in a graph GG is a kk-connected mm-dominating set ((k,m)(k,m)-cds) if the subgraph G[S]G[S] induced by SS is kk-connected and every vVSv \in V \setminus S has at least mm neighbors in SS. In the kk-Connected mm-Dominating Set ((k,m)(k,m)-CDS) problem the goal is to find a minimum weight (k,m)(k,m)-cds in a node-weighted graph. For mkm \geq k we obtain the following approximation ratios. For general graphs our ratio O(klnn)O(k \ln n) improves the previous best ratio O(k2lnn)O(k^2 \ln n) and matches the best known ratio for unit weights. For unit disc graphs we improve the ratio O(klnk)O(k \ln k) to min{mmk,k2/3}O(ln2k)\min\left\{\frac{m}{m-k},k^{2/3}\right\} \cdot O(\ln^2 k) -- this is the first sublinear ratio for the problem, and the first polylogarithmic ratio O(ln2k)/ϵO(\ln^2 k)/\epsilon when m(1+ϵ)km \geq (1+\epsilon)k; furthermore, we obtain ratio min{mmk,k}O(ln2k)\min\left\{\frac{m}{m-k},\sqrt{k}\right\} \cdot O(\ln^2 k) for uniform weights. These results are obtained by showing the same ratios for the Subset kk-Connectivity problem when the set TT of terminals is an mm-dominating set with mkm \geq k

    A logarithmic approximation algorithm for the activation edge multicover problem

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    In the Activation Edge-Multicover problem we are given a multigraph G=(V,E)G=(V,E) with activation costs {ceu,cev}\{c_{e}^u,c_{e}^v\} for every edge e=uvEe=uv \in E, and degree requirements r={rv:vV}r=\{r_v:v \in V\}. The goal is to find an edge subset JEJ \subseteq E of minimum activation cost vVmax{cuvv:uvJ}\sum_{v \in V}\max\{c_{uv}^v:uv \in J\},such that every vVv \in V has at least rvr_v neighbors in the graph (V,J)(V,J). Let k=maxvVrvk= \max_{v \in V} r_v be the maximum requirement and let θ=maxe=uvEmax{ceu,cev}min{ceu,cev}\theta=\max_{e=uv \in E} \frac{\max\{c_e^u,c_e^v\}}{\min\{c_e^u,c_e^v\}} be the maximum quotient between the two costs of an edge. For θ=1\theta=1 the problem admits approximation ratio O(logk)O(\log k). For k=1k=1 it generalizes the Set Cover problem (when θ=\theta=\infty), and admits a tight approximation ratio O(logn)O(\log n). This implies approximation ratio O(klogn)O(k \log n) for general kk and θ\theta, and no better approximation ratio was known. We obtain the first logarithmic approximation ratio O(logk+logmin{θ,n})O(\log k +\log\min\{\theta,n\}), that bridges between the two known ratios -- O(logk)O(\log k) for θ=1\theta=1 and O(logn)O(\log n) for k=1k=1. This implies approximation ratio O(logk+logmin{θ,n})+β(θ+1)O\left(\log k +\log\min\{\theta,n\}\right) +\beta \cdot (\theta+1) for the Activation kk-Connected Subgraph problem, where β\beta is the best known approximation ratio for the ordinary min-cost version of the problem

    Data Structures for Node Connectivity Queries

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    Let κ(s,t)\kappa(s,t) denote the maximum number of internally disjoint paths in an undirected graph GG. We consider designing a data structure that includes a list of cuts, and answers the following query: given s,tVs,t \in V, determine whether κ(s,t)k\kappa(s,t) \leq k, and if so, return a pointer to an stst-cut of size k\leq k (or to a minimum stst-cut) in the list. A trivial data structure that includes a list of n(n1)/2n(n-1)/2 cuts and requires Θ(kn2)\Theta(kn^2) space can answer each query in O(1)O(1) time. We obtain the following results. In the case when GG is kk-connected, we show that nn cuts suffice, and that these cuts can be partitioned into (2k+1)(2k+1) laminar families. Thus using space O(kn)O(kn) we can answers each min-cut query in O(1)O(1) time, slightly improving and substantially simplifying a recent result of Pettie and Yin. We then extend this data structure to subset kk-connectivity. In the general case we show that (2k+1)n(2k+1)n cuts suffice to return an stst-cut of size k\leq k,and a list of size k(k+2)nk(k+2)n contains a minimum stst-cut for every s,tVs,t \in V. Combining our subset kk-connectivity data structure with the data structure of Hsu and Lu for checking kk-connectivity, we give an O(k2n)O(k^2 n) space data structure that returns an stst-cut of size k\leq k in O(logk)O(\log k) time, while O(k3n)O(k^3 n) space enables to return a minimum stst-cut

    Data Structures for Node Connectivity Queries

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    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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