Random generation of combinatorial structures from a uniform
Theoretical Computer Science
How hard is it to marry at random? (On the approximation of the permanent)
STOC '86 Proceedings of the eighteenth annual ACM symposium on Theory of computing
SIAM Journal on Computing
Randomized algorithms
Random Structures & Algorithms
Tracking Many Objects with Many Sensors
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries
Journal of the ACM (JACM)
Accelerating simulated annealing for the permanent and combinatorial counting problems
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Fourier-information duality in the identity management problem
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Approximating the permanent with fractional belief propagation
The Journal of Machine Learning Research
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Approximation of the permanent of a matrix with nonnegative entries is a well studied problem. The most successful approach to date for general matrices uses Markov chains to approximately sample from a distribution on weighted permutations, and Jerrum, Sinclair, and Vigoda developed such a method they proved runs in polynomial time in the input. The current bound on the running time of their method is O(n7(log n)4). Here we present a very different approach using sequential acceptance/rejection, and show that for a class of dense problems this method has an O(n4 log n) expected running time.