Convergence of Stochastic Approximation Monte Carlo and modified Wang-Landau algorithms: Tests for the Ising model

Abstract

We investigate the behavior of the deviation of the estimator for the density of states (DOS) with respect to the exact solution in the course of Wang-Landau and Stochastic Approximation Monte Carlo (SAMC) simulations of the two-dimensional Ising model. We find that the deviation saturates in the Wang-Landau case. This can be cured by adjusting the refinement scheme. To this end, the 1/t-modification of the Wang-Landau algorithm has been suggested. A similar choice of refinement scheme is employed in the SAMC algorithm. The convergence behavior of all three algorithms is examined. It turns out that the convergence of the SAMC algorithm is very sensitive to the onset of the refinement. Finally, the internal energy and specific heat of the Ising model are calculated from the SAMC DOS and compared to exact values.

References in corpus (6)

• Optimizing the ensemble for equilibration in broad-histogram Monte Carlo simulations
• Wang-Landau Algorithm: a Theoretical Analysis of the Saturation of the Error
• Versatile approach to access the low temperature thermodynamics of lattice polymers and proteins
• Analysis of the convergence of the 1/t and Wang-Landau algorithms in the calculation of multidimensional integrals
• Optimal Modification Factor and Convergence of the Wang-Landau Algorithm
• Optimized Wang-Landau sampling of lattice polymers: Ground state search and folding thermodynamics of HP model proteins

Cited by in corpus (2)

• First-order transitions and thermodynamic properties in the 2D Blume-Capel model: the transfer-matrix method revisited
• Control of accuracy in the Wang-Landau algorithm