451 citations · 474 across the 6 of their papers we have counts for
6 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…
Derivation of QUBO formulations for sparse estimation
Tomohiro Yokota, Makiko Konoshima, Hirotaka Tamura +1
We propose a quadratic unconstrained binary optimization (QUBO) formulation of the l1-norm, which enables us to perform sparse estimation of Ising-type annealing methods such as qu…
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…
Quadratic unconstrained binary optimization formulation for rectified-linear-unit-type functions
Go Sato, Makiko Konoshima, Takuya Ohwa +2
We propose a quadratic unconstrained binary optimization (QUBO) formulation of rectified linear unit (ReLU) type functions. Different from the q-loss function proposed by Denchev e…
Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer
Maliheh Aramon, Gili Rosenberg, Elisabetta Valiante +3
The Fujitsu Digital Annealer (DA) is designed to solve fully connected quadratic unconstrained binary optimization (QUBO) problems. It is implemented on application-specific CMOS h…