The Wang-Landau algorithm reaches the flat histogram in finite time.

MCMC practitioners may be familiar with the Wang-Landau algorithm, which is widely used in Physics. This algorithm divides the sample space into “boxes”. Given a target distribution, the algorithm then samples proportionally to the target in each box, while aiming at spending a pre-defined proportion of the sample in each box. (Usually these predefined proportions are uniform.)

This strategy can help move faster between modes of a distribution, by forcing the sample to visit often the space between modes.

The most sophisticated versions of this algorithm combine a decreasing stochastic schedule and the so-called flat histogram criterion: whenever the proportions of the sample in each box are close enough to the desired frequencies, the stochastic schedule decreases. A decreasing schedule is necessary for diminishing adaptation to hold.

Until now, it was unknown whether the flat histogram is necessarily reached in finite time, and hence whether the schedule ever starts decreasing.

Pierre Jacob and I just submitted and arXived a proof that the flat histogram is reached in finite time under some conditions, and may never be reached in other cases.


2 Responses to “The Wang-Landau algorithm reaches the flat histogram in finite time.”

  1. Pierre Jacob’s viva « Robin Ryder's blog Says:

    […] Pierre Jacob defended his PhD on Monday, which was also the first time I was on the examiner bench at a PhD viva (much less stressful than being the candidate!). Unsurprisingly, the viva went very well, with laudatory reports. His thesis is a collection of 5(!) papers he co-wrote, including Free energy SMC, SMC², Block independent Methopolis-Hastings, Parallel Adaptive Wang-Landau (my personal favourite), and our collaboration on the Wang-Landau Flat Histogram. […]

  2. Accepted: Wang-Landau Flat Histogram « Robin Ryder's blog Says:

    […] details, see this blog post or read the paper on […]

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