Comparing Monte Carlo methods for finding ground states of Ising spin glasses: Population annealing, simulated annealing, and parallel tempering.
Population annealing is a Monte Carlo algorithm that marries features from simulated-annealing and parallel-tempering Monte Carlo. As such, it is ideal to overcome large energy barriers in the free-energy landscape while minimizing a Hamiltonian. Thus, population-annealing Monte Carlo can be used as a heuristic to solve combinatorial optimization problems. We illustrate the capabilities of population-annealing Monte Carlo by computing ground states of the three-dimensional Ising spin glass with Gaussian disorder, while comparing to simulated-annealing and parallel-tempering Monte Carlo. Our results suggest that population annealing Monte Carlo is significantly more efficient than simulated annealing but comparable to parallel-tempering Monte Carlo for finding spin-glass ground states.
Department of Physics, University of Massachusetts, Amherst, Massachusetts 01003, USA.
Wenlong
W
Wang
Department of Physics, University of Massachusetts, Amherst, Massachusetts 01003, USA.|Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA.
Jonathan
J
Machta
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA.|Department of Physics and Astronomy, Texas A&M University, College Station, Texas 77843-4242, USA.|Materials Science and Engineering, Texas A&M University, College Station, Texas 77843, USA.
Helmut G
HG
Katzgraber