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A Markov Jump Process for More Efficient Hamiltonian Monte Carlo
In most sampling algorithms, including Hamiltonian Monte Carlo, transiti...
09/13/2015 ∙ by Andrew B. Berger, et al. ∙
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Automatic Differentiable Monte Carlo: Theory and Application
Differentiable programming has emerged as a key programming paradigm emp...
11/20/2019 ∙ by Shi-Xin Zhang, et al. ∙
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Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs
Hamiltonian Monte Carlo (HMC) is the dominant statistical inference algo...
04/07/2018 ∙ by Bradley Gram-Hansen, et al. ∙
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Constraining Effective Field Theories with Machine Learning
We present powerful new analysis techniques to constrain effective field...
04/30/2018 ∙ by Johann Brehmer, et al. ∙
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Towards reduction of autocorrelation in HMC by machine learning
In this paper we propose new algorithm to reduce autocorrelation in Mark...
12/11/2017 ∙ by Akinori Tanaka, et al. ∙
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Bayesian Constraint Relaxation
Prior information often takes the form of parameter constraints. Bayesia...
01/04/2018 ∙ by Leo L. Duan, et al. ∙
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Combining Monte-Carlo and Hyper-heuristic methods for the Multi-mode Resource-constrained Multi-project Scheduling Problem
Multi-mode resource and precedence-constrained project scheduling is a w...
11/13/2015 ∙ by Shahriar Asta, et al. ∙
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Constrained Hybrid Monte Carlo algorithms for gauge-Higgs models
08/07/2019 ∙ by Michael Günther, et al. ∙
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We present the construction of Hybrid Monte Carlo (HMC) algorithms for constrained Hamiltonian systems of gauge-Higgs models in order to measure the constraint effective Higgs potential. In particular we focus on SU(2) Gauge-Higgs Unification models in five dimensions, where the Higgs field is identified with (some of) the five-dimensional components of the gauge field. Previous simulations have identified regions in the Higgs phase of these models which have properties of 4D adjoint or Abelian gauge-Higgs models. We develop new methods to measure constraint effective potentials, using an extension of the so-called Rattle algorithm to general Hamiltonians for constrained systems, which we adapt to the 4D Abelian gauge-Higgs model and the 5D SU(2) gauge theory on the torus and on the orbifold. The derivative of the potential is determined via the expectation value of the Lagrange multiplier for the constraint. To our knowledge, this is the first time this problem has been addressed for theories with gauge fields. The algorithm can also be used in four dimensions to study finite temperature and density transitions via effective Polyakov loop actions.
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