info:eu-repo/semantics/article
Nonconvergence of the Wang-Landau algorithms with multiple random walkers
Fecha
2016-05Registro en:
Belardinelli, Rolando Elio; Pereyra, Victor Daniel; Nonconvergence of the Wang-Landau algorithms with multiple random walkers; American Physical Society; Physical Review E; 93; 5; 5-2016; 1-9; 053306
2470-0053
CONICET Digital
CONICET
Autor
Belardinelli, Rolando Elio
Pereyra, Victor Daniel
Resumen
This paper discusses some convergence properties in the entropic sampling Monte Carlo methods with multiple random walkers, particularly in the Wang-Landau (WL) and 1/t algorithms. The classical algorithms are modified by the use of m-independent random walkers in the energy landscape to calculate the density of states (DOS). The Ising model is used to show the convergence properties in the calculation of the DOS, as well as the critical temperature, while the calculation of the number π by multiple dimensional integration is used in the continuum approximation. In each case, the error is obtained separately for each walker at a fixed time, t; then, the average over m walkers is performed. It is observed that the error goes as 1/m. However, if the number of walkers increases above a certain critical value m>mx, the error reaches a constant value (i.e., it saturates). This occurs for both algorithms; however, it is shown that for a given system, the 1/t algorithm is more efficient and accurate than the similar version of the WL algorithm. It follows that it makes no sense to increase the number of walkers above a critical value mx, since it does not reduce the error in the calculation. Therefore, the number of walkers does not guarantee convergence.