14 citations · 18 across the 4 of their papers we have counts for
4 papers
Sampling via Rejection-Free Partial Neighbor Search
Sigeng Chen, Jeffrey S. Rosenthal, Aki Dote +2
The Metropolis algorithm involves producing a Markov chain to converge to a specified target density . In order to improve its efficiency, we can use the Rejection-Free version…
Optimization via Rejection-Free Partial Neighbor Search
Sigeng Chen, Jeffrey S. Rosenthal, Aki Dote +2
Simulated Annealing using Metropolis steps at decreasing temperatures is widely used to solve complex combinatorial optimization problems. In order to improve its efficiency, we ca…
Graph clustering with Boltzmann machines
Pierre Miasnikof, Mohammad Bagherbeik, Ali Sheikholeslami
Graph clustering is the process of grouping vertices into densely connected sets called clusters. We tailor two mathematical programming formulations from the literature, to this p…
Jump Markov Chains and Rejection-Free Metropolis Algorithms
J. S. Rosenthal, A. Dote, K. Dabiri +3
We consider versions of the Metropolis algorithm which avoid the inefficiency of rejections. We first illustrate that a natural Uniform Selection Algorithm might not converge to th…