Zero-Variance Principle for Monte Carlo Algorithms

Roland Assaraf and Michel Caffarel
Phys. Rev. Lett. 83, 4682 – Published 6 December 1999
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Abstract

We present a general approach to greatly increase at little cost the efficiency of Monte Carlo algorithms. To each observable to be computed we associate a renormalized observable (improved estimator) having the same average but a different variance. By writing down the zero-variance condition a fundamental equation determining the optimal choice for the renormalized observable is derived (zero-variance principle for each observable separately). We show, with several examples including classical and quantum Monte Carlo calculations, that the method can be very powerful.

  • Received 7 June 1999

DOI:https://doi.org/10.1103/PhysRevLett.83.4682

©1999 American Physical Society

Authors & Affiliations

Roland Assaraf* and Michel Caffarel

  • CNRS-Laboratoire de Chimie Théorique Tour 22-23, Université Pierre et Marie Curie, 4 place Jussieu, 75252 Paris Cedex 05, France

  • *Email address: ra@lct.jussieu.fr
  • Email address: mc@lct.jussieu.fr

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Issue

Vol. 83, Iss. 23 — 6 December 1999

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