Improvement of Monte Carlo estimates with covariance-optimized finite-size scaling at fixed phenomenological coupling

Physics – Condensed Matter – Statistical Mechanics

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

5 pages, 1 figure, 2 tables; v2: slightly changed title, improved presentation, results unchanged

Scientific paper

10.1103/PhysRevE.84.025703

In the finite-size scaling analysis of Monte Carlo data, instead of computing the observables at fixed Hamiltonian parameters, one may choose to keep a renormalization-group invariant quantity, also called phenomenological coupling, fixed at a given value. Within this scheme of finite-size scaling, we exploit the statistical covariance between the observables in a Monte Carlo simulation in order to reduce the statistical errors of the quantities involved in the computation of the critical exponents. This method is general and does not require additional computational time. This approach is demonstrated in the Ising model in two and three dimensions, where large gain factors in CPU time are obtained.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Improvement of Monte Carlo estimates with covariance-optimized finite-size scaling at fixed phenomenological coupling does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Improvement of Monte Carlo estimates with covariance-optimized finite-size scaling at fixed phenomenological coupling, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Improvement of Monte Carlo estimates with covariance-optimized finite-size scaling at fixed phenomenological coupling will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-218889

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.