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...

It is illustrated for 4D SU(2) lattice gauge theory that sampling with a biased Metropolis scheme is essentially equivalent to using the heatbath algorithm. Only, the biased Metropolis method can also be applied when an efficient heatbath algorithm does not exist. Other cases for which the use of the biased Metropolis-heatbath algorithm is beneficial are briefly summarized.

The partition function of two dimensional massless staggered fermions interacting with U(N) gauge fields is rewritten in terms of loop variables in the strong coupling limit. We use this representation of the theory to devise a non-local Metropolis algorithm to calculate the chiral susceptibility. For small lattices our algorithm reproduces exact results quite accurately. Applying this algorith...

We deene a general methodology to deal with a large family of scheduling problems. We consider the case when some of the constraints are expressed through the minimization of a loss function. We study with more details a benchmark example consisting in some jigsaw puzzle problem with additional constraints. This is the occasion to discuss some algorithmic issues typical of scheduling problems, ...

While the presence of clustering in crime and security event data is well established, the mechanism(s) by which clustering arises is not fully understood. Both contagion models and history independent correlation models are applied, but not simultaneously. In an attempt to disentangle contagion from other types of correlation, we consider a Hawkes process with background rate driven by a log G...

Let ~ sn ∈ {0, 1}1xN be an arbitrary sequence1 in an equilibrium ensemble ofM sequences sampled according to Hint[~s] using the Metropolis Monte Carlo (MC) algorithm [1]. This ensemble is comprised of sequences from six independent realizations of 10 steps each, where each step constitutes an attempt to flip a single site. We eliminated the first 10 steps for equilibration, and recorded every i...

We congratulate Luke Tierney for this paper, which even before its appearance has done a valuable service in clarifying both theory and practice in this important area. For example, the discussion of combining strategies in Section 2.4 helped researchers break away from pure Gibbs sampling in 1991; it was, for example, part of the reasoning that lead to the \Metropolis-coupled" scheme of Geyer ...

The goal of this experiment was to create Monte Carlo simulations of the 1D and 2D Ising model. To accomplish this the Metropolis algorithm was implemented in MATLAB. The dependence of magnetization on temperature with and without an external field was calculated, as well as the dependence of the energy, specific heat, and magnetic susceptibility on temperature. The results of the 2D simulation...

Application of the Metropolis algorithm for forest harvest scheduling is extended by automating the relative weighting of objective function components. Previous applications of the Metropolis algorithm require the user to specify these weights, which demands substantial trial and error in practice. This modification allows for general incorporation of objective function components that are eit...

This paper gives geometric tools: comparison, Nash and Sobolev inequalities for pieces of the relevent Markov operators, that give useful bounds on rates of convergence for the Metropolis algorithm. As an example, we treat the random placement of N hard discs in the unit square, the original application of the Metropolis algorithm.