For the 1D harmonic chain, event-chain Monte Carlo at the zero-pressure point reaches O(N^{3/2}) autocorrelation-time scaling, beating Hamiltonian Monte Carlo's O(N^{9/4}) and local Metropolis's O(N^3).
Event-chain Monte Carlo and the true self-avoiding walk
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abstract
We study the large-scale dynamics of event chain Monte Carlo algorithms in one dimension, and their relation to the true self-avoiding walk. In particular, we study the influence of stress, and different forms of interaction on the equilibration and sampling properties of algorithms with global balance, but no local balance.
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cond-mat.stat-mech 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
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Hamiltonian Monte Carlo vs. event-chain Monte Carlo: an appraisal of sampling strategies beyond the diffusive regime
For the 1D harmonic chain, event-chain Monte Carlo at the zero-pressure point reaches O(N^{3/2}) autocorrelation-time scaling, beating Hamiltonian Monte Carlo's O(N^{9/4}) and local Metropolis's O(N^3).