3,075 research outputs found

    Parameterized Complexity of Equitable Coloring

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    A graph on nn vertices is equitably kk-colorable if it is kk-colorable and every color is used either n/k\left\lfloor n/k \right\rfloor or n/k\left\lceil n/k \right\rceil times. Such a problem appears to be considerably harder than vertex coloring, being NP-Complete\mathsf{NP\text{-}Complete} even for cographs and interval graphs. In this work, we prove that it is W[1]-Hard\mathsf{W[1]\text{-}Hard} for block graphs and for disjoint union of split graphs when parameterized by the number of colors; and W[1]-Hard\mathsf{W[1]\text{-}Hard} for K1,4K_{1,4}-free interval graphs when parameterized by treewidth, number of colors and maximum degree, generalizing a result by Fellows et al. (2014) through a much simpler reduction. Using a previous result due to Dominique de Werra (1985), we establish a dichotomy for the complexity of equitable coloring of chordal graphs based on the size of the largest induced star. Finally, we show that \textsc{equitable coloring} is FPT\mathsf{FPT} when parameterized by the treewidth of the complement graph

    Explicit linear kernels via dynamic programming

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    Several algorithmic meta-theorems on kernelization have appeared in the last years, starting with the result of Bodlaender et al. [FOCS 2009] on graphs of bounded genus, then generalized by Fomin et al. [SODA 2010] to graphs excluding a fixed minor, and by Kim et al. [ICALP 2013] to graphs excluding a fixed topological minor. Typically, these results guarantee the existence of linear or polynomial kernels on sparse graph classes for problems satisfying some generic conditions but, mainly due to their generality, it is not clear how to derive from them constructive kernels with explicit constants. In this paper we make a step toward a fully constructive meta-kernelization theory on sparse graphs. Our approach is based on a more explicit protrusion replacement machinery that, instead of expressibility in CMSO logic, uses dynamic programming, which allows us to find an explicit upper bound on the size of the derived kernels. We demonstrate the usefulness of our techniques by providing the first explicit linear kernels for rr-Dominating Set and rr-Scattered Set on apex-minor-free graphs, and for Planar-\mathcal{F}-Deletion on graphs excluding a fixed (topological) minor in the case where all the graphs in \mathcal{F} are connected.Comment: 32 page

    Hitting forbidden minors: Approximation and Kernelization

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    We study a general class of problems called F-deletion problems. In an F-deletion problem, we are asked whether a subset of at most kk vertices can be deleted from a graph GG such that the resulting graph does not contain as a minor any graph from the family F of forbidden minors. We obtain a number of algorithmic results on the F-deletion problem when F contains a planar graph. We give (1) a linear vertex kernel on graphs excluding tt-claw K1,tK_{1,t}, the star with tt leves, as an induced subgraph, where tt is a fixed integer. (2) an approximation algorithm achieving an approximation ratio of O(log3/2OPT)O(\log^{3/2} OPT), where OPTOPT is the size of an optimal solution on general undirected graphs. Finally, we obtain polynomial kernels for the case when F contains graph θc\theta_c as a minor for a fixed integer cc. The graph θc\theta_c consists of two vertices connected by cc parallel edges. Even though this may appear to be a very restricted class of problems it already encompasses well-studied problems such as {\sc Vertex Cover}, {\sc Feedback Vertex Set} and Diamond Hitting Set. The generic kernelization algorithm is based on a non-trivial application of protrusion techniques, previously used only for problems on topological graph classes

    Structurally Parameterized d-Scattered Set

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    In dd-Scattered Set we are given an (edge-weighted) graph and are asked to select at least kk vertices, so that the distance between any pair is at least dd, thus generalizing Independent Set. We provide upper and lower bounds on the complexity of this problem with respect to various standard graph parameters. In particular, we show the following: - For any d2d\ge2, an O(dtw)O^*(d^{\textrm{tw}})-time algorithm, where tw\textrm{tw} is the treewidth of the input graph. - A tight SETH-based lower bound matching this algorithm's performance. These generalize known results for Independent Set. - dd-Scattered Set is W[1]-hard parameterized by vertex cover (for edge-weighted graphs), or feedback vertex set (for unweighted graphs), even if kk is an additional parameter. - A single-exponential algorithm parameterized by vertex cover for unweighted graphs, complementing the above-mentioned hardness. - A 2O(td2)2^{O(\textrm{td}^2)}-time algorithm parameterized by tree-depth (td\textrm{td}), as well as a matching ETH-based lower bound, both for unweighted graphs. We complement these mostly negative results by providing an FPT approximation scheme parameterized by treewidth. In particular, we give an algorithm which, for any error parameter ϵ>0\epsilon > 0, runs in time O((tw/ϵ)O(tw))O^*((\textrm{tw}/\epsilon)^{O(\textrm{tw})}) and returns a d/(1+ϵ)d/(1+\epsilon)-scattered set of size kk, if a dd-scattered set of the same size exists

    Sufficient Conditions for Tuza's Conjecture on Packing and Covering Triangles

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    Given a simple graph G=(V,E)G=(V,E), a subset of EE is called a triangle cover if it intersects each triangle of GG. Let νt(G)\nu_t(G) and τt(G)\tau_t(G) denote the maximum number of pairwise edge-disjoint triangles in GG and the minimum cardinality of a triangle cover of GG, respectively. Tuza conjectured in 1981 that τt(G)/νt(G)2\tau_t(G)/\nu_t(G)\le2 holds for every graph GG. In this paper, using a hypergraph approach, we design polynomial-time combinatorial algorithms for finding small triangle covers. These algorithms imply new sufficient conditions for Tuza's conjecture on covering and packing triangles. More precisely, suppose that the set TG\mathscr T_G of triangles covers all edges in GG. We show that a triangle cover of GG with cardinality at most 2νt(G)2\nu_t(G) can be found in polynomial time if one of the following conditions is satisfied: (i) νt(G)/TG13\nu_t(G)/|\mathscr T_G|\ge\frac13, (ii) νt(G)/E14\nu_t(G)/|E|\ge\frac14, (iii) E/TG2|E|/|\mathscr T_G|\ge2. Keywords: Triangle cover, Triangle packing, Linear 3-uniform hypergraphs, Combinatorial algorithm
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