Self-Avoiding Random Dynamics on Integer Complex Systems

By Firas Hamze, Ziyun Wang, Nando de Freitas. 2011

25 citations recorded by the source corpus.

This article introduces a new specialized algorithm for equilibrium Monte Carlo sampling of binary-valued systems, which allows for large moves in the state space. This is achieved by constructing self-avoiding walks (SAWs) in the state space. As a consequence, many bits are flipped in a single MCMC step. We name the algorithm SARDONICS, an acronym for Self-Avoiding Random Dynamics on Integer Complex Systems. The algorithm has several free parameters, but we show that Bayesian optimization can be used to automatically tune them. SARDONICS performs remarkably well in a broad number of sampling tasks: toroidal ferromagnetic and frustrated Ising models, 3D Ising models, restricted Boltzmann machines and chimera graphs arising in the design of quantum computers.

Subfields: Atomic and Molecular Physics, and Optics, Condensed Matter Physics, Statistics and Probability

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Citations in the corpus

Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, background): “Returning to the drug application, we can imagine the effectiveness of different drugs (arms on the bandit) as being determined by a function f that takes an index a ∈ 1 , . . . , K and returns a Bernoulli parameter in the interval (0 , 1) .”
Adaptive MCMC with Bayesian Optimization (2012, methodology): “Very recently, we demonstrated that the technique scales reasonably well to moderately high dimensions (Hamze et al., 2011; Hoffman et al., 2011) and that it can be applied to adapt Hamiltonian (hybrid) Monte Carlo, when using prediction accuracy on test data as the objective function (Wang & de…”
On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning (2014, methodology): “Bayesian optimization techniques have already been proposed to automate machine learning approaches, such as MCMC inference [Mahendran et al., 2012, Hamze et al., 2013, Wang et al., 2013a], deep learning [Bergstra et al., 2011], preference learning [Brochu et al., 2007, 2010a], reinforcement…”