Swendsen–Wang algorithm

The Swendsen–Wang algorithm is the first non-local or cluster algorithm for Monte Carlo simulation for large systems near criticality. It has been introduced by Robert Swendsen and Jian-Sheng Wang in 1987 at Carnegie Mellon.

The original algorithm was designed for the Ising and Potts models, and it was later generalized to other systems as well, such as the XY model by Wolff algorithm and particles of fluids. The key ingredient was the random cluster model, a representation of the Ising or Potts model through percolation models of connecting bonds, due to Fortuin and Kasteleyn. It has been generalized by Barbu and Zhu (2005) to arbitrary sampling probabilities by viewing it as a Metropolis–Hastings algorithm and computing the acceptance probability of the proposed Monte Carlo move.

Motivation

The problem of the critical slowing-down affecting local processes is of fundamental importance in the study of second-order phase transitions (like ferromagnetic transition in the Ising model), as increasing the size of the system in order to reduce finite-size effects has the disadvantage of requiring a far larger number of moves to reach thermal equilibrium. Indeed the correlation time $\tau$ usually increases as $L^{z}$ with $z\simeq 2$ or greater; since, to be accurate, the simulation time must be $t\gg \tau$, this is a major limitation in the size of the systems that can be studied through local algorithms. SW algorithm was the first to produce unusually small values for the dynamical critical exponents: $z=0.35$ for the 2D Ising model ($z=2.125$ for standard simulations); $z=0.75$ for the 3D Ising model, as opposed to $z=2.0$ for standard simulations.

Description

Main article: Random cluster model

The algorithm is non-local in the sense that in a single sweep of moves a collective update of the spin variables of the system is done. The key idea is to take an additional number of 'bond' variables, as suggested by Fortuin and Kasteleyn, who mapped the Potts model onto a percolation model via the random cluster model.

Consider a typical ferromagnetic Ising model with only nearest-neighbor interaction.

• Starting from a given configuration of spins, we associate to each pair of nearest neighbours on sites $n,m$ a random variable $b_{n,m}\in \lbrace 0,1\rbrace$ which is interpreted in the following way: if $b_{n,m}=0$ there is no link between the sites $n$ and $m$; if $b_{n,m}=1$, $\sigma _{n}$ and $\sigma _{m}$ are connected. These values are assigned according to the following (conditional) probability distribution:

 $P\left[b_{n,m}=0|\sigma _{n}\neq \sigma _{m}\right]=1$;
 $P\left[b_{n,m}=1|\sigma _{n}\neq \sigma _{m}\right]=0$;
 $P\left[b_{n,m}=0|\sigma _{n}=\sigma _{m}\right]=e^{-2\beta J_{nm}}$;
 $P\left[b_{n,m}=1|\sigma _{n}=\sigma _{m}\right]=1-e^{-2\beta J_{nm}}$;

where $J_{nm}>0$ is the ferromagnetic interaction intensity.

This probability distribution has been derived in the following way: the Hamiltonian of the Ising model is

$H[\sigma ]=\sum \limits _{<i,j>}-J_{i,j}\sigma _{i}\sigma _{j}$,

and the partition function is

$Z=\sum \limits _{\lbrace \sigma \rbrace }e^{-\beta H[\sigma ]}$.

Consider the interaction between a pair of selected sites $n$ and $m$ and eliminate it from the total Hamiltonian, defining $H_{nm}[\sigma ]=\sum \limits _{<i,j>\neq <n,m>}-J_{i,j}\sigma _{i}\sigma _{j}.$

Define also the restricted sums:

$Z_{n,m}^{same}=\sum \limits _{\lbrace \sigma \rbrace }e^{-\beta H_{nm}[\sigma ]}\delta _{\sigma _{n},\sigma _{m}}$;

$Z_{n,m}^{diff}=\sum \limits _{\lbrace \sigma \rbrace }e^{-\beta H_{nm}[\sigma ]}\left(1-\delta _{\sigma _{n},\sigma _{m}}\right).$

$Z=e^{\beta J_{nm}}Z_{n,m}^{same}+e^{-\beta J_{nm}}Z_{n,m}^{diff}.$

Introduce the quantity

$Z_{nm}^{ind}=Z_{n,m}^{same}+Z_{n,m}^{diff}$;

the partition function can be rewritten as

$Z=\left(e^{\beta J_{nm}}-e^{-\beta J_{nm}}\right)Z_{n,m}^{same}+e^{-\beta J_{nm}}Z_{n,m}^{ind}.$

Since the first term contains a restriction on the spin values whereas there is no restriction in the second term, the weighting factors (properly normalized) can be interpreted as probabilities of forming/not forming a link between the sites: $P_{<n,m>\;link}=1-e^{-2\beta J_{nm}}.$ The process can be easily adapted to antiferromagnetic spin systems, as it is sufficient to eliminate $Z_{n,m}^{same}$ in favor of $Z_{n,m}^{diff}$ (as suggested by the change of sign in the interaction constant).

• After assigning the bond variables, we identify the same-spin clusters formed by connected sites and make an inversion of all the variables in the cluster with probability 1/2. At the following time step we have a new starting Ising configuration, which will produce a new clustering and a new collective spin-flip.

Correctness

It can be shown that this algorithm leads to equilibrium configurations. The first way to prove it is using the theory of Markov chains, either noting that the equilibrium (described by Boltzmann-Gibbs distribution) maps into itself, or showing that in a single sweep of the lattice there is a non-zero probability of going from any state of the Markov chain to any other; thus the corresponding irreducible ergodic Markov chain has an asymptotic probability distribution satisfying detailed balance.

Alternatively, we can show explicitly that detailed balance is satisfied. Every transition between two Ising configurations must pass through some bond configuration in the percolation representation. Let's fix a particular bond configuration: what matters in comparing the probabilities related to it is the number of factors $q=e^{-2\beta J}$ for each missing bond between neighboring spins with the same value; the probability of going to a certain Ising configuration compatible with a given bond configuration is uniform (say $p$). So the ratio of the transition probabilities of going from one state to another is

${\frac {P_{\lbrace \sigma \rbrace \rightarrow \lbrace \sigma '\rbrace }}{P_{\lbrace \sigma '\rbrace \rightarrow \lbrace \sigma \rbrace }}}={\frac {Pr\left(\lbrace \sigma '\rbrace |B.C.\right)Pr\left(B.C.|\lbrace \sigma \rbrace \right)}{Pr\left(\lbrace \sigma \rbrace |B.C.\right)Pr\left(B.C.|\lbrace \sigma '\rbrace \right)}}={\frac {p\cdot \exp \left[-2\beta \sum \limits _{<l,m>}\delta _{\sigma _{l},\sigma _{m}}J_{lm}\right]}{p\cdot \exp \left[-2\beta \sum \limits _{<l,m>}\delta _{\sigma '_{l},\sigma '_{m}}J_{lm}\right]}}=e^{-\beta \Delta E}$

since $\Delta E=-\sum \limits _{<l,m>}J_{lm}\left(\sigma '_{l}\sigma '_{m}-\sigma _{l}\sigma _{m}\right)=-\sum \limits _{<l,m>}J_{lm}\left[\delta _{\sigma '_{l},\sigma '_{m}}-\left(1-\delta _{\sigma '_{l},\sigma '_{m}}\right)-\delta _{\sigma _{l},\sigma _{m}}+\left(1-\delta _{\sigma _{l},\sigma _{m}}\right)\right]=-2\sum \limits _{<l,m>}J_{lm}\left(\delta _{\sigma '_{l},\sigma '_{m}}-\delta _{\sigma _{l},\sigma _{m}}\right)$.

This is valid for every bond configuration the system can pass through during its evolution, so detailed balance is satisfied for the total transition probability. This proves that the algorithm works.

Efficiency

Although not analytically clear from the original paper, the reason why all the values of z obtained with the SW algorithm are much lower than the exact lower bound for single-spin-flip algorithms ($z\geq \gamma /\nu$) is that the correlation length divergence is strictly related to the formation of percolation clusters, which are flipped together. In this way the relaxation time is significantly reduced.

The algorithm is not efficient in simulating frustrated systems.

See Also

• Random cluster model
• Monte Carlo method
• http://www.hpjava.org/theses/shko/thesis_paper/node69.html
• http://www-fcs.acs.i.kyoto-u.ac.jp/~harada/monte-en.html

References

• Swendsen, Robert H.; Wang, Jian-Sheng (1987-01-12). "Nonuniversal critical dynamics in Monte Carlo simulations". Physical Review Letters. 58 (2). American Physical Society (APS): 86–88. doi:10.1103/physrevlett.58.86. ISSN 0031-9007.
• Kasteleyn P. W. and Fortuin (1969) J. Phys. Soc. Jpn. Suppl. 26s:11
• Fortuin, C.M.; Kasteleyn, P.W. (1972). "On the random-cluster model". Physica. 57 (4). Elsevier BV: 536–564. doi:10.1016/0031-8914(72)90045-6. ISSN 0031-8914.
• Wang, Jian-Sheng; Swendsen, Robert H. (1990). "Cluster Monte Carlo algorithms". Physica A: Statistical Mechanics and its Applications. 167 (3). Elsevier BV: 565–579. doi:10.1016/0378-4371(90)90275-w. ISSN 0378-4371.
• Barbu, A. (2005). "Generalizing Swendsen-Wang to sampling arbitrary posterior probabilities". IEEE Transactions on Pattern Analysis and Machine Intelligence. 27 (8). Institute of Electrical and Electronics Engineers (IEEE): 1239–1253. doi:10.1109/tpami.2005.161. ISSN 0162-8828.