Monte Carlo Quasi-Heatbath by approximate inversion

Abstract

When sampling the distribution P(phi) ~ exp(-|A phi|^2), a global heatbath normally proceeds by solving the linear system A phi = eta, where eta is a normal Gaussian vector, exactly. This paper shows how to preserve the distribution P(phi) while solving the linear system with arbitrarily low accuracy. Generalizations are presented.

10 pages, 1 figure; typos corrected, reference added; version to appear in Phys. Rev. E

References in corpus (3)

• Stochastic Estimation with $Z_2$ Noise
• Evaluating Sea Quark Contributions to Flavour-Singlet Operators in Lattice QCD
• Improving Stochastic Estimator Techniques for Disconnected Diagrams

Cited by in corpus (13)

• Effective noise reduction techniques for disconnected loops in Lattice QCD
• The nucleon's strange electromagnetic and scalar matrix elements
• Efficient Gaussian Sampling for Solving Large-Scale Inverse Problems using MCMC Methods
• Progress in lattice algorithms
• The Kentucky Noisy Monte Carlo Algorithm for Wilson Dynamical Fermions
• Efficiency of the UV-filtered Multiboson algorithm
• Disconnected Loop Subtraction Methods in Lattice QCD
• Schr"odinger functional at negative flavour number
• Improved Multiboson Algorithm
• Conjugate gradient heatbath for ill-conditioned actions
• The Quest for Light Sea Quarks: Algorithms for the Future
• Unquenched domain wall quarks with multi-bosons
• Exploiting the hopping parameter expansion in the hybrid Monte Carlo (HMC) simulation of lattice QCD with two degenerate flavours of Wilson fermions