Wang and Landau algorithm

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The Wang and Landau algorithm, proposed by Fugao Wang and David P. Landau,[1] is a Monte Carlo method designed to calculate the density of states of a system. The method performs a non-Markovian random walk to build the density of states by quickly visiting all the available energy spectrum. The Wang and Landau algorithm is an important method to obtain the density of states required to perform a multicanonical simulation.

The Wang–Landau algorithm can be applied to any system which is characterized by a cost (or energy) function. For instance, it has been applied to the solution of numerical integrals[2] and the folding of proteins.[3][4] The Wang-Landau sampling is related to the metadynamics algorithm.[5]


The Wang and Landau algorithm is used to obtain the density of states of a system characterized by a cost function. It uses a non-Markovian stochastic process which asymptotically converges to a multicanonical ensemble.[1] (I.e. to a Metropolis-Hastings algorithm with sampling distribution inverse to the density of states.) The major consequence is that this sampling distribution leads to a simulation where the energy barriers are invisible. This means that the algorithm visits all the accessible states (favorable and less favorable) much faster than a Metropolis algorithm.[6]


Consider a system defined on a phase space , and a cost function, E, (e.g. the energy), bounded on a spectrum , which has an associated density of states , which is to be computed. Because Wang and Landau algorithm works in discrete spectra,[1] the spectrum is divided in N discrete values with a difference between them of , such that


Given this discrete spectrum, the algorithm is initialized by:

  • setting all entries of the microcanonical entropy to zero,
  • initializing and
  • initializing the system randomly, by putting in a random configuration .

The algorithm then performs a multicanonical ensemble simulation:[1] a Metropolis-Hastings random walk in the phase space of the system with a probability distribution given by and a probability of proposing a new state given by a probability distribution . A histogram of visited energies is stored. Like in the Metropolis-Hastings algorithm, a proposal-acceptance step is performed, and consists in (see Metropolis–Hastings algorithm overview):

  1. proposing a state according to the arbitrary proposal distribution
  2. accept/refuse the proposed state according to
where and .

After each proposal-acceptance step, the system transits to some value , is incremented by one and the following update is performed:


This is the crucial step of the algorithm, and it is what makes the Wang and Landau algorithm non-Markovian: the stochastic process now depends on the history of the process. Hence the next time there is a proposal to a state with that particular energy , that proposal is now more likely refused; in this sense, the algorithm forces the system to visit all of the spectrum equally.[1] The consequence is that the histogram is more and more flat. However, this flatness depends on how well-approximated the calculated entropy is to the exact entropy, which naturally depends on the value of f.[7] To better and better approximate the exact entropy (and thus histogram's flatness), f is decreased after M proposal-acceptance steps:


It was later shown that updating the f by constantly dividing by two can lead to saturation errors.[7] A small modification to the Wang and Landau method to avoid this problem is to use the f factor proportional to , where is proportional to the number of steps of the simulation.[7]

Test system[edit]

We want to obtain the DOS for the harmonic oscillator potential.

The analytical DOS is given by,

by performing the last integral we obtain

In general, the DOS for a multidimensional harmonic oscillator will be given by some power of E, the exponent will be a function of the dimension of the system.

Hence, we can use a simple harmonic oscillator potential to test the accuracy of Wang–Landau algorithm because we know already the analytic form of the density of states. Therefore, we compare the density of states obtained by the Wang–Landau algorithm with .

Sample code[edit]

The following is a sample code of the Wang–Landau algorithm in Python, where we assume that a symmetric proposal distribution g is used:

The code considers a "system" which is the underlying system being studied.

currentEnergy = system.randomConfiguration() # a random initial configuration

while (f > epsilon):
    system.proposeConfiguration() # a proposed configuration is proposed
    proposedEnergy = system.proposedEnergy() # the energy of the proposed configuration computed

    if (random() < exp(entropy[currentEnergy]-entropy[proposedEnergy])):
        # if accepted, update the energy and the system:
        currentEnergy = proposedEnergy
        # if rejected
    H[currentEnergy] += 1
    entropy[currentEnergy] += f
    if (isFlat(H)): # isFlat tests whether the histogram is flat (e.g. 95% flatness)
        H[:] = 0
        f *= 0.5 # refine the f parameter

Wang and Landau molecular dynamics[edit]

It should be noted that the Wang and Landau algorithm can be implemented not only in a Monte Carlo simulation but also in a molecular dynamics simulation. To do this would require an escalation of the temperature of the system as follows:

where is the entropy of the system, the micro-canonical temperature and is the "scaled" temperature used in the simulation.


  1. ^ a b c d e Wang, Fugao & Landau, D. P. (Mar 2001). "Efficient, Multiple-Range Random Walk Algorithm to Calculate the Density of States". Phys. Rev. Lett. American Physical Society. 86 (10): 2050–2053. Bibcode:2001PhRvL..86.2050W. PMID 11289852. arXiv:cond-mat/0011174Freely accessible. doi:10.1103/PhysRevLett.86.2050. 
  2. ^ R. E. Belardinelli and S. Manzi and V. D. Pereyra (Dec 2008). "Analysis of the convergence of the 1∕t and Wang–Landau algorithms in the calculation of multidimensional integrals". Phys. Rev. E. American Physical Society. 78 (6): 067701. Bibcode:2008PhRvE..78f7701B. arXiv:0806.0268Freely accessible. doi:10.1103/PhysRevE.78.067701. 
  3. ^ P. Ojeda and M. Garcia and A. Londono and N.Y. Chen (Feb 2009). "Monte Carlo Simulations of Proteins in Cages: Influence of Confinement on the Stability of Intermediate States". Biophys. J. Biophysical Society. 96 (3): 1076–1082. Bibcode:2009BpJ....96.1076O. doi:10.1529/biophysj.107.125369. 
  4. ^ P. Ojeda & M. Garcia (Jul 2010). "Electric Field-Driven Disruption of a Native beta-Sheet Protein Conformation and Generation of alpha-Helix-Structure". Biophys. J. Biophysical Society. 99 (2): 595–599. Bibcode:2009BpJ....96.1076O. PMC 2905109Freely accessible. PMID 20643079. doi:10.1016/j.bpj.2010.04.040. 
  5. ^ Christoph Junghans, Danny Perez, and Thomas Vogel. "Molecular Dynamics in the Multicanonical Ensemble: Equivalence of Wang–Landau Sampling, Statistical Temperature Molecular Dynamics, and Metadynamics." Journal of Chemical Theory and Computation 10.5 (2014): 1843-1847. doi:10.1021/ct500077d
  6. ^ Berg, B.; Neuhaus, T. (1992). "Multicanonical ensemble: A new approach to simulate first-order phase transitions". Physical Review Letters. 68 (1): 9–12. Bibcode:1992PhRvL..68....9B. PMID 10045099. arXiv:hep-lat/9202004Freely accessible. doi:10.1103/PhysRevLett.68.9. 
  7. ^ a b c Belardinelli, R. E. & Pereyra, V. D. (2007). "Wang–Landau algorithm: A theoretical analysis of the saturation of the error". Jour. Chem. Phys. 127 (18): 184105. Bibcode:2007JChPh.127r4105B. arXiv:cond-mat/0702414Freely accessible. doi:10.1063/1.2803061.