1,372 research outputs found

    Matching

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    The Matching Problem in General Graphs is in Quasi-NC

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    We show that the perfect matching problem in general graphs is in Quasi-NC. That is, we give a deterministic parallel algorithm which runs in O(log3n)O(\log^3 n) time on nO(log2n)n^{O(\log^2 n)} processors. The result is obtained by a derandomization of the Isolation Lemma for perfect matchings, which was introduced in the classic paper by Mulmuley, Vazirani and Vazirani [1987] to obtain a Randomized NC algorithm. Our proof extends the framework of Fenner, Gurjar and Thierauf [2016], who proved the analogous result in the special case of bipartite graphs. Compared to that setting, several new ingredients are needed due to the significantly more complex structure of perfect matchings in general graphs. In particular, our proof heavily relies on the laminar structure of the faces of the perfect matching polytope.Comment: Accepted to FOCS 2017 (58th Annual IEEE Symposium on Foundations of Computer Science

    A simple MAX-CUT algorithm for planar graphs

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    The max-cut problem asks for partitioning the nodes V of a graph G=(V,E) into two sets (one of which might be empty), such that the sum of weights of edges joining nodes in different partitions is maximum. Whereas for general instances the max-cut problem is NP-hard, it is polynomially solvable for certain classes of graphs. For planar graphs, there exist several polynomial-time methods determining maximum cuts for arbitrary choice of edge weights. Typically, the problem is solved by computing a minimum-weight perfect matching in some associated graph. In this work, we present a new and simple algorithm for determining maximum cuts for arbitrary weighted planar graphs. Its running time can be bounded by O(|V|^(1.5)log|V|), similar to the fastest known methods. However, our transformation yields a much smaller associated graph than that of the known methods. Furthermore, it can be computed fast. As the practical running time strongly depends on the size of the associated graph, it can be expected that our algorithm is considerably faster than the methods known in the literature. More specifically, our program can determine maximum cuts in huge realistic and random planar graphs with up to 10^6 nodes

    A simple MAX-CUT algorithm for planar graphs

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    The max-cut problem asks for partitioning the nodes V of a graph G=(V,E) into two sets (one of which might be empty), such that the sum of weights of edges joining nodes in different partitions is maximum. Whereas for general instances the max-cut problem is NP-hard, it is polynomially solvable for certain classes of graphs. For planar graphs, there exist several polynomial-time methods determining maximum cuts for arbitrary choice of edge weights. Typically, the problem is solved by computing a minimum-weight perfect matching in some associated graph. In this work, we present a new and simple algorithm for determining maximum cuts for arbitrary weighted planar graphs. Its running time can be bounded by O(|V|^(1.5)log|V|), similar to the fastest known methods. However, our transformation yields a much smaller associated graph than that of the known methods. Furthermore, it can be computed fast. As the practical running time strongly depends on the size of the associated graph, it can be expected that our algorithm is considerably faster than the methods known in the literature. More specifically, our program can determine maximum cuts in huge realistic and random planar graphs with up to 10^6 nodes

    Partitioning planar graphs: a fast combinatorial approach for max-cut

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    The max-cut problem asks for partitioning the nodes V of a graph G=(V,E) into two sets (one of which might be empty), such that the sum of weights of edges joining nodes in different partitions is maximum. Whereas for general instances the max-cut problem is NP-hard, it is polynomially solvable for certain classes of graphs. For planar graphs, there exist several polynomial-time methods determining maximum cuts for arbitrary choice of edge weights. Typically, the problem is solved by computing a minimum-weight perfect matching in some associated graph. The most efficient known algorithms are those of Shih et al. and that of Berman et al. The running time of the former can be bounded by O(|V|^(3/2)log|V|). The latter algorithm is more generally for determining T-joins in graphs. Although it has a slightly larger bound on the running time of O(V{\ensuremath{|}}{\^{ }}(3/2)(log{\ensuremath{|}}V{\ensuremath{|}}){\^{ }}(3/2))alpha({\ensuremath{|}}V{\ensuremath{|}}), where alpha({\ensuremath{|}}V{\ensuremath{|}}) is the inverse Ackermann function, it can solve large instances in practice. In this work, we present a new and simple algorithm for determining maximum cuts for arbitrary weighted planar graphs. Its running time is bounded by O({\ensuremath{|}}V{\ensuremath{|}}{\^{ }}(3/2)log{\ensuremath{|}}V{\ensuremath{|}}), similar to the bound achieved by Shih et al. It can easily determine maximum cuts in huge random as well as real-world graphs with up to 10{\^{ }}6 nodes. We present experimental results for our method using two different matching implementations. We furthermore compare our approach with those of Shih et al. and Berman et al. It turns out that our algorithm is considerably faster in practice than the one by Shih et al. Moreover, it yields a much smaller associated graph. Its expanded graph size is comparable to that of Berman et al. However, whereas the procedure of generating the expanded graph in Berman et al. is very involved (thus needs a sophisticated implementation), implementing our approach is an easy and straightforward task

    An extensive English language bibliography on graph theory and its applications, supplement 1

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    Graph theory and its applications - bibliography, supplement

    A Fixed-Parameter Algorithm for the Max-Cut Problem on Embedded 1-Planar Graphs

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    We propose a fixed-parameter tractable algorithm for the \textsc{Max-Cut} problem on embedded 1-planar graphs parameterized by the crossing number kk of the given embedding. A graph is called 1-planar if it can be drawn in the plane with at most one crossing per edge. Our algorithm recursively reduces a 1-planar graph to at most 3k3^k planar graphs, using edge removal and node contraction. The \textsc{Max-Cut} problem is then solved on the planar graphs using established polynomial-time algorithms. We show that a maximum cut in the given 1-planar graph can be derived from the solutions for the planar graphs. Our algorithm computes a maximum cut in an embedded 1-planar graph with nn nodes and kk edge crossings in time O(3kn3/2logn)\mathcal{O}(3^k \cdot n^{3/2} \log n).Comment: conference version from IWOCA 201

    A Message Passing Algorithm for the Minimum Cost Multicut Problem

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    We propose a dual decomposition and linear program relaxation of the NP -hard minimum cost multicut problem. Unlike other polyhedral relaxations of the multicut polytope, it is amenable to efficient optimization by message passing. Like other polyhedral elaxations, it can be tightened efficiently by cutting planes. We define an algorithm that alternates between message passing and efficient separation of cycle- and odd-wheel inequalities. This algorithm is more efficient than state-of-the-art algorithms based on linear programming, including algorithms written in the framework of leading commercial software, as we show in experiments with large instances of the problem from applications in computer vision, biomedical image analysis and data mining.Comment: Added acknowledgment
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