Nonergodicity of local, length-conserving Monte Carlo algorithms for the self-avoiding walk

1987 ◽  
Vol 47 (3-4) ◽  
pp. 573-595 ◽  
Author(s):  
Neal Madras ◽  
Alan D. Sokal
Keyword(s):  
Monte Carlo ◽  
The Self ◽  
Monte Carlo Algorithms ◽  
Self Avoiding Walk

New Monte Carlo method for the self-avoiding walk

1985 ◽  
Vol 40 (3-4) ◽  
pp. 483-531 ◽  
Author(s):  
Alberto Berretti ◽  
Alan D. Sokal
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
The Self ◽  
Self Avoiding Walk

Monte Carlo Algorithms for Moments of Transition Arrays

1988 ◽  
Vol 102 ◽  
pp. 79-81
Author(s):  
A. Goldberg ◽  
S.D. Bloom
Keyword(s):  
Monte Carlo ◽  
State Vector ◽  
Basis Vector ◽  
Collective State ◽  
Dispersion Characteristics ◽  
Dipole Strength ◽  
The Third ◽  
Monte Carlo Algorithms ◽  
Third Moment ◽  
Very High

AbstractClosed expressions for the first, second, and (in some cases) the third moment of atomic transition arrays now exist. Recently a method has been developed for getting to very high moments (up to the 12th and beyond) in cases where a “collective” state-vector (i.e. a state-vector containing the entire electric dipole strength) can be created from each eigenstate in the parent configuration. Both of these approaches give exact results. Herein we describe astatistical(or Monte Carlo) approach which requires onlyonerepresentative state-vector |RV> for the entire parent manifold to get estimates of transition moments of high order. The representation is achieved through the random amplitudes associated with each basis vector making up |RV>. This also gives rise to the dispersion characterizing the method, which has been applied to a system (in the M shell) with≈250,000 lines where we have calculated up to the 5th moment. It turns out that the dispersion in the moments decreases with the size of the manifold, making its application to very big systems statistically advantageous. A discussion of the method and these dispersion characteristics will be presented.

scholarly journals Moment-Preserving and Mesh-Adaptive Reweighting Method for Rare-Event Sampling in Monte-Carlo Algorithms

2021 ◽  
pp. 108041
Author(s):  
C.U. Schuster ◽  
T. Johnson ◽  
G. Papp ◽  
R. Bilato ◽  
S. Sipilä ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Rare Event ◽  
Monte Carlo Algorithms
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