NEW MONTE-CARLO ALGORITHM - ENTROPIC SAMPLING

We present a new Monte Carlo sampling algorithm, with which one can obtain any desired distribution of the sampling in one Monte Carlo simulation. The free energy and the entropy of a system can thus be obtained from a simple exercise of this algorithm. The main idea is to sample directly the entropy of a system at infinite temperature. Importance sampling is shown to be a particular case of the new algorithm. The algorithm is tested against the exact partition function of the L = 4 simple cubic Ising model. A comparison with the multicanonical ensemble for the L = 12, q = 10 Potts model shows that the new algorithm is more general and more efficient.