A hybrid algorithm is proposed for pure SU(N) lattice gauge theory based on Genetic Algorithms (GA)s and the Metropolis method. We apply the hybrid GA to pure SU(2) gauge theory on a 2-dimensional lattice and find the action per plaquette and Wilson loops being consistent with those given by the Metropolis and Heatbath methods. The thermalization of this newly proposed Hybrid GA is quite faster...

Interrelationships, origin and phylogenetic affinities of brown trout Salmo trutta populations from the southern Caspian Sea basin, Orumieh and Namak Lake basins in Iran were analysed from complete mtDNA control region sequences, 12 microsatellite loci and morphological characters. Among 129 specimens from six populations, seven haplotypes were observed. Based on mtDNA haplotype data, the Orumi...

The waste-recycling Monte Carlo (WRMC) algorithm introduced by physicists is a modification of the (multi-proposal) Metropolis–Hastings algorithm, which makes use of all the proposals in the empirical mean, whereas the standard (multi-proposal) Metropolis–Hastings algorithm uses only the accepted proposals. In this paper we extend the WRMC algorithm to a general control variate technique and ex...

When a graph can be decomposed into clusters of well connected subgraphs, it is possible to speed up random walks taking advantage of the topology of the graph. In this work, a new random walk scheme is introduced and a condition is given when the new random walk performs better than the Metropolis algorithm.

We illustrate for 4D SU(2) and U(1) lattice gauge theory that sampling with a biased Metropolis scheme is essentially equivalent to using the heat bath algorithm. Only, the biased Metropolis method can also be applied when an efficient heat bath algorithm does not exist. For the examples discussed the biased Metropolis algorithm is also better suited for parallelization than the heat bath algor...

The waste-recycling Monte Carlo (WR) algorithm introduced by physicists is a modification of the (multi-proposal) Metropolis-Hastings algorithm, which makes use of all the proposals in the empirical mean, whereas the standard (multi-proposal) Metropolis-Hastings algorithm only uses the accepted proposals. In this paper, we extend the WR algorithm into a general control variate technique and exh...

Monte Carlo (MC) sampling methods are widely applied in Bayesian inference, system simulation and optimization problems. The Markov Chain Monte Carlo (MCMC) algorithms are a well-known class of MC methods which generate a Markov chain with the desired invariant distribution. In this document, we focus on the Metropolis-Hastings (MH) sampler, which can be considered as the atom of the MCMC techn...

Necessary and su cient conditions for geometric con vergence in the relative supremum norm of the Metropolis Hastings simulation algorithm with a general generating function are estab lished An explicit expression for the convergence rate is given Introduction This paper discusses the convergence rate for the Metropolis Hastings simulation algorithm proposed in Hastings The Metropolis Hastings ...