10 research outputs found

    The stackelberg minimum spanning tree game

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    We consider a one-round two-player network pricing game, the Stackelberg Minimum Spanning Tree game or STACKMST. The game is played on a graph (representing a network), whose edges are colored either red or blue, and where the red edges have a given fixed cost (representing the competitor's prices). The first player chooses an assignment of prices to the blue edges, and the second player then buys the cheapest possible minimum spanning tree, using any combination of red and blue edges. The goal of the first player is to maximize the total price of purchased blue edges. This game is the minimum spanning tree analog of the well-studied Stackelberg shortest-path game. We analyze the complexity and approximability of the first player's best strategy in STACKMST. In particular, we prove that the problem is APX-hard even if there are only two different red costs, and give an approximation algorithm whose approximation ratio is at most min{k, 3 + 2 In 6,1 + In W}, where k is the number of distinct red costs, b is the number of blue edges, and W is the maximum ratio between red costs. We also give a natural integer linear programming formulation of the problem, and show that the integrality gap of the fractional relaxation asymptotically matches the approximation guarantee of our algorithm. © Springer-Verlag Berlin Heidelberg 2007.Frank Dehne, Jörg-Rüdiger Sack, and Norbert Zeh, editors, Algorithms and Data StructuresSpringer Berlin / Heidelberg10th International Workshop on Algorithms and Data Structures, WADS 2007; Halifax; Canada; 15 August 2007 through 17 August 2007.info:eu-repo/semantics/publishe

    Entangling macroscopic quantum states

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    Spatial entanglements of macroscopic quantum systems are proposed. The which-path uncertainty of a single photon passing through a beam splitter is transformed into the which-path uncertainty of two macroscopic fields via two quantum nondemolition measurements. The macroscopic fields are nonlocally correlated
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