1,194 research outputs found
Fast Algorithms at Low Temperatures via Markov Chains
For spin systems, such as the hard-core model on independent sets weighted by fugacity lambda>0, efficient algorithms for the associated approximate counting/sampling problems typically apply in the high-temperature region, corresponding to low fugacity. Recent work of Jenssen, Keevash and Perkins (2019) yields an FPTAS for approximating the partition function (and an efficient sampling algorithm) on bounded-degree (bipartite) expander graphs for the hard-core model at sufficiently high fugacity, and also the ferromagnetic Potts model at sufficiently low temperatures. Their method is based on using the cluster expansion to obtain a complex zero-free region for the partition function of a polymer model, and then approximating this partition function using the polynomial interpolation method of Barvinok. We present a simple discrete-time Markov chain for abstract polymer models, and present an elementary proof of rapid mixing of this new chain under sufficient decay of the polymer weights. Applying these general polymer results to the hard-core and ferromagnetic Potts models on bounded-degree (bipartite) expander graphs yields fast algorithms with running time O(n log n) for the Potts model and O(n^2 log n) for the hard-core model, in contrast to typical running times of n^{O(log Delta)} for algorithms based on Barvinok\u27s polynomial interpolation method on graphs of maximum degree Delta. In addition, our approach via our polymer model Markov chain is conceptually simpler as it circumvents the zero-free analysis and the generalization to complex parameters. Finally, we combine our results for the hard-core and ferromagnetic Potts models with standard Markov chain comparison tools to obtain polynomial mixing time for the usual spin system Glauber dynamics restricted to even and odd or "red" dominant portions of the respective state spaces
Exact sampling from non-attractive distributions using summary states
Propp and Wilson's method of coupling from the past allows one to efficiently
generate exact samples from attractive statistical distributions (e.g., the
ferromagnetic Ising model). This method may be generalized to non-attractive
distributions by the use of summary states, as first described by Huber. Using
this method, we present exact samples from a frustrated antiferromagnetic
triangular Ising model and the antiferromagnetic q=3 Potts model. We discuss
the advantages and limitations of the method of summary states for practical
sampling, paying particular attention to the slowing down of the algorithm at
low temperature. In particular, we show that such a slowing down can occur in
the absence of a physical phase transition.Comment: 5 pages, 6 EPS figures, REVTeX; additional information at
http://wol.ra.phy.cam.ac.uk/mackay/exac
Generalized-ensemble simulations and cluster algorithms
The importance-sampling Monte Carlo algorithm appears to be the universally
optimal solution to the problem of sampling the state space of statistical
mechanical systems according to the relative importance of configurations for
the partition function or thermal averages of interest. While this is true in
terms of its simplicity and universal applicability, the resulting approach
suffers from the presence of temporal correlations of successive samples
naturally implied by the Markov chain underlying the importance-sampling
simulation. In many situations, these autocorrelations are moderate and can be
easily accounted for by an appropriately adapted analysis of simulation data.
They turn out to be a major hurdle, however, in the vicinity of phase
transitions or for systems with complex free-energy landscapes. The critical
slowing down close to continuous transitions is most efficiently reduced by the
application of cluster algorithms, where they are available. For first-order
transitions and disordered systems, on the other hand, macroscopic energy
barriers need to be overcome to prevent dynamic ergodicity breaking. In this
situation, generalized-ensemble techniques such as the multicanonical
simulation method can effect impressive speedups, allowing to sample the full
free-energy landscape. The Potts model features continuous as well as
first-order phase transitions and is thus a prototypic example for studying
phase transitions and new algorithmic approaches. I discuss the possibilities
of bringing together cluster and generalized-ensemble methods to combine the
benefits of both techniques. The resulting algorithm allows for the efficient
estimation of the random-cluster partition function encoding the information of
all Potts models, even with a non-integer number of states, for all
temperatures in a single simulation run per system size.Comment: 15 pages, 6 figures, proceedings of the 2009 Workshop of the Center
of Simulational Physics, Athens, G
Algorithmic Pirogov-Sinai theory
We develop an efficient algorithmic approach for approximate counting and
sampling in the low-temperature regime of a broad class of statistical physics
models on finite subsets of the lattice and on the torus
. Our approach is based on combining contour
representations from Pirogov-Sinai theory with Barvinok's approach to
approximate counting using truncated Taylor series. Some consequences of our
main results include an FPTAS for approximating the partition function of the
hard-core model at sufficiently high fugacity on subsets of with
appropriate boundary conditions and an efficient sampling algorithm for the
ferromagnetic Potts model on the discrete torus at
sufficiently low temperature
Algorithms for #BIS-hard problems on expander graphs
We give an FPTAS and an efficient sampling algorithm for the high-fugacity
hard-core model on bounded-degree bipartite expander graphs and the
low-temperature ferromagnetic Potts model on bounded-degree expander graphs.
The results apply, for example, to random (bipartite) -regular graphs,
for which no efficient algorithms were known for these problems (with the
exception of the Ising model) in the non-uniqueness regime of the infinite
-regular tree. We also find efficient counting and sampling algorithms
for proper -colorings of random -regular bipartite graphs when
is sufficiently small as a function of
Algorithms for #BIS-hard problems on expander graphs
We give an FPTAS and an efficient sampling algorithm for the high-fugacity hard-core model on bounded-degree bipartite expander graphs and the low-temperature ferromagnetic Potts model on bounded-degree expander graphs. The results apply, for example, to random (bipartite) Δ-regular graphs, for which no efficient algorithms were known for these problems (with the exception of the Ising model) in the non-uniqueness regime of the infinite Δ-regular tree
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