
Random Algorithms for the Loop Cutset Problem
We show how to find a minimum loop cutset in a Bayesian network with hig...
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De(con)struction of the lazyF loop: improving performance of Smith Waterman alignment
Striped variation of the SmithWaterman algorithm is known as extremely ...
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Randomized Algorithms for the Loop Cutset Problem
We show how to find a minimum weight loop cutset in a Bayesian network w...
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On Heuristics for Finding Loop Cutsets in MultiplyConnected Belief Networks
We introduce a new heuristic algorithm for the problem of finding minimu...
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Efficient Approximation for Triangulation of Minimum Treewidth
We present four novel approximation algorithms for finding triangulation...
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Loop Programming Practices that Simplify Quicksort Implementations
Quicksort algorithm with Hoare's partition scheme is traditionally imple...
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A Variational Loop Shrinking Analogy for Handle and Tunnel Detection and Reeb Graph Construction on Surfaces
The humble loop shrinking property played a central role in the inceptio...
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Approximation Algorithms for the Loop Cutset Problem
We show how to find a small loop curser in a Bayesian network. Finding such a loop cutset is the first step in the method of conditioning for inference. Our algorithm for finding a loop cutset, called MGA, finds a loop cutset which is guaranteed in the worst case to contain less than twice the number of variables contained in a minimum loop cutset. We test MGA on randomly generated graphs and find that the average ratio between the number of instances associated with the algorithms' output and the number of instances associated with a minimum solution is 1.22.
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