We consider the optimal discrimination of bipartite quantum states and provide an upper bound for maximum success probability local discrimination. also a necessary sufficient condition measurement to realize bound. further establish this be saturated. Finally, we illustrate our results using example.

Given a planar point set, we consider three classes of optimal triangulations: (1) the minimum weight triangulation with angular constraints (constraints on the minimum angle and the maximum angle in a triangulation), (2) the angular balanced triangulation which minimizes the sum of the ratios of the maximum angle to the minimum angle for each triangle, and (3) the area balanced triangulation w...

We consider a capacitated max k-cut problem in which a set of vertices is partitioned into k subsets. Each edge has a non-negative weight, and each subset has a possibly different capacity that imposes an upper bound on its size. The objective is to find a partition that maximizes the sum of edge weights across all pairs of vertices that lie in different subsets. We describe a local-search algo...

In this paper we use different decimation strategies in irregular pyramid segmentation framework, to produce perceptually important groupings. These graph decimation strategies, based on the maximum independent set concept, used in Bor̊uvka’s minimum spanning tree based partitioning method, show similar discrepancy segmentation errors. Global and local consistency error measures do not show big ...

This paper presents an algorithm that achieves hyper-arc consistency for the soft alldifferent constraint. To this end, we prove and exploit the equivalence with a minimum-cost flow problem. Consistency of the constraint can be checked in O(nm) time, and hyper-arc consistency is achieved in O(m) time, where n is the number of variables involved and m is the sum of the cardinalities of the domai...

When analysing binary data, the ease at which one can interpret results is very important. Many existing methods, however, discover either models that are difficult to read, or return so many results interpretation becomes impossible. Here, we study a fully automated approach for mining easily interpretable models for binary data. We model data hierarchically with noisy tiles—rectangles with si...