The Peculiar Phase Structure of Random Graph Bisection

The mincut graph bisection problem involves partitioning the n vertices of a graph into disjoint subsets, each containing exactly n/2 vertices, while minimizing the number of "cut" edges with an endpoint in each subset. When considered over sparse random graphs, the phase structure of the graph bisection problem displays certain familiar properties, but also some surprises. It is known that when the mean degree is below the critical value of 2 log 2, the cutsize is zero with high probability. We study how the minimum cutsize increases with mean degree above this critical threshold, finding a new analytical upper bound that improves considerably upon previous bounds. Combined with recent results on expander graphs, our bound suggests the unusual scenario that random graph bisection is replica symmetric up to and beyond the critical threshold, with a replica symmetry breaking transition possibly taking place above the threshold. An intriguing algorithmic consequence is that although the problem is NP-hard, we can find near-optimal cutsizes (whose ratio to the optimal value approaches 1 asymptotically) in polynomial time for typical instances near the phase transition.Comment: substantially revised section 2, changed figures 3, 4 and 6, made minor stylistic changes and added reference

The Complexity of Sharing a Pizza

Assume you have a 2-dimensional pizza with 2n ingredients that you want to share with your friend. For this you are allowed to cut the pizza using several straight cuts, and then give every second piece to your friend. You want to do this fairly, that is, your friend and you should each get exactly half of each ingredient. How many cuts do you need? It was recently shown using topological methods that n cuts always suffice. In this work, we study the computational complexity of finding such n cuts. Our main result is that this problem is PPA-complete when the ingredients are represented as point sets. For this, we give a new proof that for point sets n cuts suffice, which does not use any topological methods. We further prove several hardness results as well as a higher-dimensional variant for the case where the ingredients are well-separated

Graph bisection algorithms

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1986.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.Bibliography: leaves 64-66.by Thang Nguyen Bui.Ph.D

On the minimum bisection of random 3−3-regular graphs

In this paper we give new asymptotically almost sure lower and upper bounds on the bisection width of random $3-$regular graphs. The main contribution is a new lower bound on the bisection width of $0.103295n$, based on a first moment method together with a structural decomposition of the graph, thereby improving a 27 year old result of Kostochka and Melnikov. We also give a complementary upper bound of $0.139822n$, combining known spectral ideas with original combinatorial insights. Developping further this approach, with the help of Monte Carlo simulations, we obtain a non-rigorous upper bound of $0.131366n$.Comment: 48 pages, 20 figure

On partitioning problems with complex objectives

Hypergraph and graph partitioning tools are used to partition work for efficient parallelization of many sparse matrix computations. Most of the time, the objective function that is reduced by these tools relates to reducing the communication requirements, and the balancing constraints satisfied by these tools relate to balancing the work or memory requirements. Sometimes, the objective sought for having balance is a complex function of the partition. We describe some important class of parallel sparse matrix computations that have such balance objectives. For these cases, the current state of the art partitioning tools fall short of being adequate. To the best of our knowledge, there is only a single algorithmic framework in the literature to address such balance objectives. We propose another algorithmic framework to tackle complex objectives and experimentally investigate the proposed framework.Les outils de partitionnement de graphes et d'hypergraphes interviennent pour paralléliser efficacement de nombreux algorithmes liés aux matrices creuses. La plupart du temps, la fonction objectif minimisée par ces outils est liée au besoin de réduire les coûts de communication, tandis que les contraintes d'équilibre à satisfaire sont elles liées à l'équilibrage de la charge ou de la consommation mémoire. Parfois, l'objectif d'équilibre est une fonction complexe du partitionnement. Nous décrivons plusieurs applications majeures de calcul parallèle sur des matrices creuses où de telles contraintes d'équilibre apparaissent. Pour ces exemples, même les outils de partitionnement les plus pointus sont loin d'être adéquats. Pour autant que nous sachions, il n'existe dans la littérature qu'un seul cadre algorithmique qui traite ces problèmes. Nous proposons ici une nouvelle approche algorithmique et fournissons des résultats d'expériences la mettant en œuvre