Editing Cluster algorithms

'''Cluster algorithms''' are mainly used in the simulation of [[Ising Models|Ising-like models]]. The essential feature is the use of collective motions of particles (spins) in a single [[Monte Carlo]] step.
An interesting property of some of these application is the fact that the [[percolation analysis]] of the clusters can
be used to study [[phase transitions]].
== Swendsen-Wang algorithm ==
As an introductory example to the Swendsen-Wang algorithm we shall discuss the  technique (Ref 1) in the simulation of the 
[[Ising Models |Ising model]]. In one [[Monte Carlo]] step of the algorithm the following recipe is used:

# Consider every pair of interacting sites (spins). In the current configuration the [[Intermolecular pair potential |pair interaction]] can be either negative: <math> \Phi_{ij}/k_B T= -K  </math> or positive <math> \Phi_{ij}/k_B T = + K </math>,  depending on the product: <math> S_{i} S_{j} </math> (See the [[Ising Models |Ising model]] entry for  notation).
#For pairs of interacting sites (i.e. nearest neighbours) with <math> \Phi_{ij}/k_B T < 0 </math>, [[random numbers |randomly]] create a bond between the two spins with a given probability <math> p </math>, where <math> p </math> will be chosen to be a function of <math> K </math>. 
#The bonds generated in the previous step are used to build up clusters of sites (spins).
#Build up the partition of the system in the corresponding clusters of spins. In each cluster all the spins will have the same state, either <math> S = 1 </math> or <math> S = -1 </math>.
#For each cluster, independently, choose at random with equal probabilities whether or not to flip (invert the value of <math> S </math>) the whole set of spins belonging to the cluster. The bonding probability <math> p </math> is given by: <math> p = 1 - \exp [ -2 K ] </math>.

== Wolff algorithm ==
The procedure to create a given bond is the same as in the Swendsen-Wang algorithm. However in Wolff's method
the whole set of interacting pairs is not tested to generate (possible) bonds. Instead, a single cluster
is built. See Ref 2 for details. 

* The initial cluster contains one site, which is selected at random.

* Possible bonds between the initial site and  other sites of the system are tested. Bonded sites are included in the cluster.

* Recursively, one checks the existence of bonds between the new members of the cluster and sites of the system to add, if bonds are generated,  new sites to the ''growing'' cluster, until no more bonds are generated.

* At this point, the whole cluster is flipped (see above).

== Invaded Cluster Algorithm ==
The purpose of this algorithm is to locate [[critical points]] (critical temperature). So, in this case
the probability of bonding  neighbouring sites with equal spins is not set ''a priori''. (See Ref 3)
The algorithm for an Ising system with [[periodic boundary conditions]] can be implemented as follows:

Given a certain configuration of the system:

* One considers the possible bonds on the system (pairs of nearest neighbours with favourable interaction)

* Using [[random numbers]] one assigns a random  order to these possible bonds

* The possible bonds are being ''activated''  in the order fixed in the previous step (the cluster structure is watched during this process)

* The bond activation stops when one cluster percolates through the entire system (i.e. considering the periodic boundary conditions
the cluster becomes of infinite size)

* Then,  every cluster (as in the Swendsen-Wang algorithm) is flipped with proability 1/2.

* An effective bond probability for the percolation threshold, <math> p_{per} </math> can be computed as:

: <math> p_{per} = M_{act}/M </math>

with <math> M_{act}</math> being the number of activated bonds when the first cluster ''percolates'', and <math> M </math> is the number
of possible bonds.

The value of <math> p_{per} </math> (in one realisation, or the averaged value over the simulation, See the references for the practical application) can be related with the critical coupling constant, <math> k_c </math> as:

: <math> p_{per} \approx  1 - \exp \left[ - 2 k_c \right]  </math>

== Probability-Changing Cluster Algorithm ==
This method was proposed by Tomita and Okabe (See Ref 4). This procedure is orientated towards computing [[critical points]]. 
It applies when the symmetry of the interactions imply that the critical
temperature is that in which the clusters, built using a Swendsen-Wang type algorithm, reach
the percolation threshold. 
The simulation proceeds by a fine tuning of the temperature (or the coupling constant)
Given a configuration of the system and a current coupling constant <math> K_0 </math>:

* One builds  a bond realisation following the Swendsen-Wang strategy

* One establishes whether at least one of the cluster percolates through the whole system

* If percolation occurs one decreases the coupling constant (increase the temperature) by a small amount:

: <math> K^{new} = K_0 - \delta K </math>

* If no percolation appears, the new value of the coupling constant is taken to be:

:  <math> K^{new} = K_0 + \delta K </math>,

with <math>\delta K > 0 </math>. For small values of <math> \delta K </math> the value of <math> K </math>
(after reaching the vicinity of the critical point) will show minor oscillations and the
results can be trusted as those of an equilibrium simulation run. ([[Detailed balance]] is not
strictly fulfilled in this algorithm).

== Beyond the Ising and Potts models ==

The methods described so far can be used, with minor changes, in the simulation of [[Potts model|Potts models]].
In addition, extensions have been proposed in the literature 
to build up very efficient cluster algorithms to simulate more complex lattice systems (for example the [[XY model]], 
[[Heisenberg model]], etc).

== Application to continuous (atomistic) models ==

It is sometimes possible (and very convenient) to include cluster algorithms in the  simulation of
models with continuous translational degrees of freedom. In most cases the cluster algorithm has
to be complemented with other sampling moves to ensure [[ergodicity]]. Examples:

* Spin fluids
* [[Binary mixtures]] (with symmetry in the interactions)
* Continuous version of [[XY model]], [[Heisenberg model]], etc.


In these cases, the usual approach is to combine one-particle moves (e.g. particle translations), 
with cluster procedures. In the cluster steps, multiparticle modification of -composition, orientations, etc.-
is carried out.

== Other (not so smart) applications of cluster algorithms ==
Monte Carlo simulation of atomistic systems with multiparticle moves, for example see:

*[http://dx.doi.org/10.1063/1.2759924  N. G. Almarza and E. Lomba "Cluster algorithm to perform parallel Monte Carlo simulation of atomistic systems", Journal of Chemical Physics '''127''' 084116 (2007)]

== References ==
#[http://dx.doi.org/10.1103/PhysRevLett.58.86  Robert H. Swendsen and Jian-Sheng Wang, "Nonuniversal critical dynamics in Monte Carlo simulations", Physical Review Letters '''58''' pp. 86 - 88 (1987) ]
#[http://dx.doi.org/10.1103/PhysRevLett.62.361 Ulli Wolff, "Collective Monte Carlo Updating for Spin Systems" , Physical Review Letters '''62''' pp. 361 - 364 (1989) ]
#[http://dx.doi.org/10.1103/PhysRevLett.75.2792     J. Machta, Y. S. Choi, A. Lucke,  T. Schweizer, and L. V. Chayes, "Invaded Cluster Algorithm for Equilibrium Critical Points" , Physical Review Letters '''75''' pp. 2792 - 2795 (1995)]
#[http://dx.doi.org/10.1103/PhysRevLett.86.572      Yusuke Tomita and Yutaka Okabe,  "Probability-Changing Cluster Algorithm for Potts Models", Physical Review Letters '''86''' pp. 572 - 575 (2001)]