Random generation of combinatorial structures from a uniform
Theoretical Computer Science
Fast uniform generation of regular graphs
Theoretical Computer Science
Simple Markov-chain algorithms for generating bipartite graphs and tournaments
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Generating random regular graphs
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
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
State-dependent Importance Sampling and large Deviations
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
Negative examples for sequential importance sampling of binary contingency tables
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Assessing data mining results via swap randomization
ACM Transactions on Knowledge Discovery from Data (TKDD)
Approximately Counting Embeddings into Random Graphs
APPROX '08 / RANDOM '08 Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques
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We study the problem of counting and randomly sampling binary contingency tables. For given row and column sums, we are interested in approximately counting (or sampling) 0/1 n x m matrices with the specified row/column sums. We present a simulated annealing algorithm with running time O((nm)2D3dmaxlog5(n+m)) for any row/column sums where D is the number of non-zero entries and dmax is the maximum row/column sum. This is the first algorithm to directly solve binary contingency tables for all row/column sums. Previous work reduced the problem to the permanent, or restricted attention to row/column sums that are close to regular. The interesting aspect of our simulated annealing algorithm is that it starts at a non-trivial instance, whose solution relies on the existence of short alternating paths in the graph constructed by a particular Greedy algorithm.