Diffusion Monte Carlo method: Numerical Analysis in a Simple Case

Mohamed El Makrini; Benjamin Jourdain; Tony Lelièvre

ESAIM: Mathematical Modelling and Numerical Analysis (2007)

  • Volume: 41, Issue: 2, page 189-213
  • ISSN: 0764-583X

Abstract

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The Diffusion Monte Carlo method is devoted to the computation of electronic ground-state energies of molecules. In this paper, we focus on implementations of this method which consist in exploring the configuration space with a fixed number of random walkers evolving according to a stochastic differential equation discretized in time. We allow stochastic reconfigurations of the walkers to reduce the discrepancy between the weights that they carry. On a simple one-dimensional example, we prove the convergence of the method for a fixed number of reconfigurations when the number of walkers tends to +∞ while the timestep tends to 0. We confirm our theoretical rates of convergence by numerical experiments. Various resampling algorithms are investigated, both theoretically and numerically.


How to cite

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El Makrini, Mohamed, Jourdain, Benjamin, and Lelièvre, Tony. "Diffusion Monte Carlo method: Numerical Analysis in a Simple Case." ESAIM: Mathematical Modelling and Numerical Analysis 41.2 (2007): 189-213. <http://eudml.org/doc/250054>.

@article{ElMakrini2007,
abstract = {
The Diffusion Monte Carlo method is devoted to the computation of electronic ground-state energies of molecules. In this paper, we focus on implementations of this method which consist in exploring the configuration space with a fixed number of random walkers evolving according to a stochastic differential equation discretized in time. We allow stochastic reconfigurations of the walkers to reduce the discrepancy between the weights that they carry. On a simple one-dimensional example, we prove the convergence of the method for a fixed number of reconfigurations when the number of walkers tends to +∞ while the timestep tends to 0. We confirm our theoretical rates of convergence by numerical experiments. Various resampling algorithms are investigated, both theoretically and numerically.
},
author = {El Makrini, Mohamed, Jourdain, Benjamin, Lelièvre, Tony},
journal = {ESAIM: Mathematical Modelling and Numerical Analysis},
keywords = {Diffusion Monte Carlo method; interacting particle systems; ground state; Schrödinger operator; Feynman-Kac formula.},
language = {eng},
month = {6},
number = {2},
pages = {189-213},
publisher = {EDP Sciences},
title = {Diffusion Monte Carlo method: Numerical Analysis in a Simple Case},
url = {http://eudml.org/doc/250054},
volume = {41},
year = {2007},
}

TY - JOUR
AU - El Makrini, Mohamed
AU - Jourdain, Benjamin
AU - Lelièvre, Tony
TI - Diffusion Monte Carlo method: Numerical Analysis in a Simple Case
JO - ESAIM: Mathematical Modelling and Numerical Analysis
DA - 2007/6//
PB - EDP Sciences
VL - 41
IS - 2
SP - 189
EP - 213
AB - 
The Diffusion Monte Carlo method is devoted to the computation of electronic ground-state energies of molecules. In this paper, we focus on implementations of this method which consist in exploring the configuration space with a fixed number of random walkers evolving according to a stochastic differential equation discretized in time. We allow stochastic reconfigurations of the walkers to reduce the discrepancy between the weights that they carry. On a simple one-dimensional example, we prove the convergence of the method for a fixed number of reconfigurations when the number of walkers tends to +∞ while the timestep tends to 0. We confirm our theoretical rates of convergence by numerical experiments. Various resampling algorithms are investigated, both theoretically and numerically.

LA - eng
KW - Diffusion Monte Carlo method; interacting particle systems; ground state; Schrödinger operator; Feynman-Kac formula.
UR - http://eudml.org/doc/250054
ER -

References

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  1. A. Alfonsi, On the discretization schemes for the CIR (and Bessel squared) processes. Monte Carlo Methods Appl.11 (2005) 355–384.  
  2. R. Assaraf, M. Caffarel and A. Khelif, Diffusion Monte Carlo with a fixed number of walkers. Phys. Rev. E61 (2000) 4566–4575.  
  3. E. Cancès, M. Defranceschi, W. Kutzelnigg, C. Le Bris and Y. Maday, Computational Quantum Chemistry: a Primer, in Handbook of Numerical Analysis, Special volume, Computational Chemistry, volume X, Ph.G. Ciarlet and C. Le Bris Eds., North-Holland (2003) 3–270.  
  4. E. Cancès, B. Jourdain and T. Lelièvre, Quantum Monte Carlo simulations of fermions. A mathematical analysis of the fixed-node approximation. Math. Mod. Methods Appl. Sci.16 (2006) 1403–1440.  
  5. O. Cappé, R. Douc and E. Moulines, Comparison of Resampling Schemes for Particle Filtering, in 4th International Symposium on Image and Signal Processing and Analysis (ISPA), Zagreb, Croatia (2005).  
  6. N. Chopin, Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference. Ann. Statist.32 (2004) 2385–2411.  
  7. P. Del Moral, Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications. Springer-Verlag (2004).  
  8. P. Del Moral and A. Doucet, Particle motions in absorbing medium with hard and soft obstacles. Stochastic Anal. Appl.22 (2004) 1175–1207.  
  9. P. Del Moral and L. Miclo, Branching and Interacting Particle Systems. Approximation of Feynman-Kac Formulae with Applications to Non-Linear Filtering, in Séminaire de Probabilités XXXIV, Lecture Notes in Mathematics1729, Springer-Verlag (2000) 1–145.  
  10. P. Del Moral and L. Miclo, Particle approximations of Lyapunov exponents connected to Schrödinger operators and Feynman-Kac semigroups. ESAIM: PS7 (2003) 171–208.  
  11. P. Glasserman, Monte Carlo methods in financial engineering. Springer-Verlag (2004).  
  12. J.H. Hetherington, Observations on the statistical iteration of matrices. Phys. Rev. A30 (1984) 2713–2719.  
  13. P.J. Reynolds, D.M. Ceperley, B.J. Alder and W.A. Lester, Fixed-node quantum Monte Carlo for molecules. J. Chem. Phys.77 (1982) 5593–5603.  
  14. M. Rousset, On the control of an interacting particle approximation of Schrödinger groundstates. SIAM J. Math. Anal.38 (2006) 824–844.  
  15. S. Sorella, Green Function Monte Carlo with Stochastic Reconfiguration. Phys. Rev. Lett.80 (1998) 4558–4561.  
  16. D. Talay and L. Tubaro, Expansion of the global error for numerical schemes solving stochastic differential equations. Stochastic Anal. Appl.8 (1990) 94–120.  
  17. C.J. Umrigar, M.P. Nightingale and K.J. Runge, A Diffusion Monte Carlo algorithm with very small time-step errors. J. Chem. Phys.99 (1993) 2865–2890.  

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