26,975 research outputs found

    Edge-Fault Tolerance of Hypercube-like Networks

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    This paper considers a kind of generalized measure λs(h)\lambda_s^{(h)} of fault tolerance in a hypercube-like graph GnG_n which contain several well-known interconnection networks such as hypercubes, varietal hypercubes, twisted cubes, crossed cubes and M\"obius cubes, and proves λs(h)(Gn)=2h(n−h)\lambda_s^{(h)}(G_n)= 2^h(n-h) for any hh with 0⩽h⩽n−10\leqslant h\leqslant n-1 by the induction on nn and a new technique. This result shows that at least 2h(n−h)2^h(n-h) edges of GnG_n have to be removed to get a disconnected graph that contains no vertices of degree less than hh. Compared with previous results, this result enhances fault-tolerant ability of the above-mentioned networks theoretically

    Non-Uniform Robust Network Design in Planar Graphs

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    Robust optimization is concerned with constructing solutions that remain feasible also when a limited number of resources is removed from the solution. Most studies of robust combinatorial optimization to date made the assumption that every resource is equally vulnerable, and that the set of scenarios is implicitly given by a single budget constraint. This paper studies a robustness model of a different kind. We focus on \textbf{bulk-robustness}, a model recently introduced~\cite{bulk} for addressing the need to model non-uniform failure patterns in systems. We significantly extend the techniques used in~\cite{bulk} to design approximation algorithm for bulk-robust network design problems in planar graphs. Our techniques use an augmentation framework, combined with linear programming (LP) rounding that depends on a planar embedding of the input graph. A connection to cut covering problems and the dominating set problem in circle graphs is established. Our methods use few of the specifics of bulk-robust optimization, hence it is conceivable that they can be adapted to solve other robust network design problems.Comment: 17 pages, 2 figure

    The Complexity of Change

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    Many combinatorial problems can be formulated as "Can I transform configuration 1 into configuration 2, if certain transformations only are allowed?". An example of such a question is: given two k-colourings of a graph, can I transform the first k-colouring into the second one, by recolouring one vertex at a time, and always maintaining a proper k-colouring? Another example is: given two solutions of a SAT-instance, can I transform the first solution into the second one, by changing the truth value one variable at a time, and always maintaining a solution of the SAT-instance? Other examples can be found in many classical puzzles, such as the 15-Puzzle and Rubik's Cube. In this survey we shall give an overview of some older and more recent work on this type of problem. The emphasis will be on the computational complexity of the problems: how hard is it to decide if a certain transformation is possible or not?Comment: 28 pages, 6 figure
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