1,113 research outputs found
Many Masses on One Stroke: Economic Computation of Quark Propagators
The computational effort in the calculation of Wilson fermion quark
propagators in Lattice Quantum Chromodynamics can be considerably reduced by
exploiting the Wilson fermion matrix structure in inversion algorithms based on
the non-symmetric Lanczos process. We consider two such methods: QMR (quasi
minimal residual) and BCG (biconjugate gradients). Based on the decomposition
of the Wilson mass matrix, using QMR, one can carry
out inversions on a {\em whole} trajectory of masses simultaneously, merely at
the computational expense of a single propagator computation. In other words,
one has to compute the propagator corresponding to the lightest mass only,
while all the heavier masses are given for free, at the price of extra storage.
Moreover, the symmetry can be used to cut
the computational effort in QMR and BCG by a factor of two. We show that both
methods then become---in the critical regime of small quark
masses---competitive to BiCGStab and significantly better than the standard MR
method, with optimal relaxation factor, and CG as applied to the normal
equations.Comment: 17 pages, uuencoded compressed postscrip
Preconditioning Markov Chain Monte Carlo Simulations Using Coarse-Scale Models
We study the preconditioning of Markov chain Monte Carlo (MCMC) methods using coarse-scale models with applications to subsurface characterization. The purpose of preconditioning is to reduce the fine-scale computational cost and increase the acceptance rate in the MCMC sampling. This goal is achieved by generating Markov chains based on two-stage computations. In the first stage, a new proposal is first tested by the coarse-scale model based on multiscale finite volume methods. The full fine-scale computation will be conducted only if the proposal passes the coarse-scale screening. For more efficient simulations, an approximation of the full fine-scale computation using precomputed multiscale basis functions can also be used. Comparing with the regular MCMC method, the preconditioned MCMC method generates a modified Markov chain by incorporating the coarse-scale information of the problem. The conditions under which the modified Markov chain will converge to the correct posterior distribution are stated in the paper. The validity of these assumptions for our application and the conditions which would guarantee a high acceptance rate are also discussed. We would like to note that coarse-scale models used in the simulations need to be inexpensive but not necessarily very accurate, as our analysis and numerical simulations demonstrate. We present numerical examples for sampling permeability fields using two-point geostatistics. The Karhunen--Loève expansion is used to represent the realizations of the permeability field conditioned to the dynamic data, such as production data, as well as some static data. Our numerical examples show that the acceptance rate can be increased by more than 10 times if MCMC simulations are preconditioned using coarse-scale models
Numerical simulation tests with light dynamical quarks
Two degenerate flavours of quarks are simulated with small masses down to
about one fifth of the strange quark mass by using the two-step multi-boson
(TSMB) algorithm. The lattice size is 8^3 x 16 with lattice spacing about
0.27fm which is not far from the N_t=4 thermodynamical cross-over line.
Autocorrelations of different physical quantities are estimated as a function
of the quark mass. The eigenvalue spectra of the Wilson-Dirac operator are
investigated.Comment: 14 pages, 7 figures, uses svjour.cls; mistake in the autocorrelation
of the pion mass corrected, version accepted for publication on Eur. Phys. J.
A computational framework for infinite-dimensional Bayesian inverse problems: Part II. Stochastic Newton MCMC with application to ice sheet flow inverse problems
We address the numerical solution of infinite-dimensional inverse problems in
the framework of Bayesian inference. In the Part I companion to this paper
(arXiv.org:1308.1313), we considered the linearized infinite-dimensional
inverse problem. Here in Part II, we relax the linearization assumption and
consider the fully nonlinear infinite-dimensional inverse problem using a
Markov chain Monte Carlo (MCMC) sampling method. To address the challenges of
sampling high-dimensional pdfs arising from Bayesian inverse problems governed
by PDEs, we build on the stochastic Newton MCMC method. This method exploits
problem structure by taking as a proposal density a local Gaussian
approximation of the posterior pdf, whose construction is made tractable by
invoking a low-rank approximation of its data misfit component of the Hessian.
Here we introduce an approximation of the stochastic Newton proposal in which
we compute the low-rank-based Hessian at just the MAP point, and then reuse
this Hessian at each MCMC step. We compare the performance of the proposed
method to the original stochastic Newton MCMC method and to an independence
sampler. The comparison of the three methods is conducted on a synthetic ice
sheet inverse problem. For this problem, the stochastic Newton MCMC method with
a MAP-based Hessian converges at least as rapidly as the original stochastic
Newton MCMC method, but is far cheaper since it avoids recomputing the Hessian
at each step. On the other hand, it is more expensive per sample than the
independence sampler; however, its convergence is significantly more rapid, and
thus overall it is much cheaper. Finally, we present extensive analysis and
interpretation of the posterior distribution, and classify directions in
parameter space based on the extent to which they are informed by the prior or
the observations.Comment: 31 page
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