Single temperature for Monte Carlo optimization on complex landscapes.
Phys Rev Lett
; 108(25): 250602, 2012 Jun 22.
Article
em En
| MEDLINE
| ID: mdl-23004580
ABSTRACT
We propose a new strategy for Monte Carlo (MC) optimization on rugged multidimensional landscapes. The strategy is based on querying the statistical properties of the landscape in order to find the temperature at which the mean first passage time across the current region of the landscape is minimized. Thus, in contrast to other algorithms such as simulated annealing, we explicitly match the temperature schedule to the statistics of landscape irregularities. In cases where these statistics are approximately the same over the entire landscape or where nonlocal moves couple distant parts of the landscape, a single-temperature MC scheme outperforms any other MC algorithm with the same move set. We also find that in strongly anisotropic Coulomb spin glass and traveling salesman problems, the only relevant statistics (which we use to assign a single MC temperature) are those of irregularities in low-energy funnels. Our results may explain why protein folding is efficient at constant temperature.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Método de Monte Carlo
/
Modelos Teóricos
Tipo de estudo:
Health_economic_evaluation
Idioma:
En
Revista:
Phys Rev Lett
Ano de publicação:
2012
Tipo de documento:
Article
País de afiliação:
Estados Unidos