NEW MONTE-CARLO ALGORITHMS FOR INTERACTING SELF-AVOIDING WALKS

In this paper we present two new Monte Carlo algorithms for simulating interacting self-avoiding walks. These algorithms are built on the Be rretti-Sokal and reptation algorithms for simulating non-interacting s elf-avoiding walks respectively by adding a Metropolis step. We enhanc e each algorithm by introducing special local moves. In addition, the reptation algorithm is further enhanced by running the simulations as a multiple Markov chain. Each of these algorithms are studied here thr ough an analysis of their autocorrelation properties. In the case of t he Berretti-Sokal algorithm an increase in efficiency by a factor of a round 40 is achieved for non-interacting self-avoiding walks.