25,676 research outputs found
Divergence of the multilevel Monte Carlo Euler method for nonlinear stochastic differential equations
The Euler-Maruyama scheme is known to diverge strongly and numerically weakly
when applied to nonlinear stochastic differential equations (SDEs) with
superlinearly growing and globally one-sided Lipschitz continuous drift
coefficients. Classical Monte Carlo simulations do, however, not suffer from
this divergence behavior of Euler's method because this divergence behavior
happens on rare events. Indeed, for such nonlinear SDEs the classical Monte
Carlo Euler method has been shown to converge by exploiting that the Euler
approximations diverge only on events whose probabilities decay to zero very
rapidly. Significantly more efficient than the classical Monte Carlo Euler
method is the recently introduced multilevel Monte Carlo Euler method. The main
observation of this article is that this multilevel Monte Carlo Euler method
does - in contrast to classical Monte Carlo methods - not converge in general
in the case of such nonlinear SDEs. More precisely, we establish divergence of
the multilevel Monte Carlo Euler method for a family of SDEs with superlinearly
growing and globally one-sided Lipschitz continuous drift coefficients. In
particular, the multilevel Monte Carlo Euler method diverges for these
nonlinear SDEs on an event that is not at all rare but has probability one. As
a consequence for applications, we recommend not to use the multilevel Monte
Carlo Euler method for SDEs with superlinearly growing nonlinearities. Instead
we propose to combine the multilevel Monte Carlo method with a slightly
modified Euler method. More precisely, we show that the multilevel Monte Carlo
method combined with a tamed Euler method converges for nonlinear SDEs with
globally one-sided Lipschitz continuous drift coefficients and preserves its
strikingly higher order convergence rate from the Lipschitz case.Comment: Published in at http://dx.doi.org/10.1214/12-AAP890 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Multilevel Monte Carlo methods
The author's presentation of multilevel Monte Carlo path simulation at the
MCQMC 2006 conference stimulated a lot of research into multilevel Monte Carlo
methods. This paper reviews the progress since then, emphasising the
simplicity, flexibility and generality of the multilevel Monte Carlo approach.
It also offers a few original ideas and suggests areas for future research
Metropolis Methods for Quantum Monte Carlo Simulations
Since its first description fifty years ago, the Metropolis Monte Carlo
method has been used in a variety of different ways for the simulation of
continuum quantum many-body systems. This paper will consider some of the
generalizations of the Metropolis algorithm employed in quantum Monte Carlo:
Variational Monte Carlo, dynamical methods for projector monte carlo ({\it
i.e.} diffusion Monte Carlo with rejection), multilevel sampling in path
integral Monte Carlo, the sampling of permutations, cluster methods for lattice
models, the penalty method for coupled electron-ionic systems and the Bayesian
analysis of imaginary time correlation functions.Comment: Proceedings of "Monte Carlo Methods in the Physical Sciences"
Celebrating the 50th Anniversary of the Metropolis Algorith
Central limit theorems for multilevel Monte Carlo methods
In this work, we show that uniform integrability is not a necessary condition
for central limit theorems (CLT) to hold for normalized multilevel Monte Carlo
(MLMC) estimators and we provide near optimal weaker conditions under which the
CLT is achieved. In particular, if the variance decay rate dominates the
computational cost rate (i.e., ), we prove that the CLT applies
to the standard (variance minimizing) MLMC estimator.
For other settings where the CLT may not apply to the standard MLMC
estimator, we propose an alternative estimator, called the mass-shifted MLMC
estimator, to which the CLT always applies.
This comes at a small efficiency loss: the computational cost of achieving
mean square approximation error is at worst a factor
higher with the mass-shifted estimator than
with the standard one
Multilevel Monte Carlo methods for applications in finance
Since Giles introduced the multilevel Monte Carlo path simulation method
[18], there has been rapid development of the technique for a variety of
applications in computational finance. This paper surveys the progress so far,
highlights the key features in achieving a high rate of multilevel variance
convergence, and suggests directions for future research.Comment: arXiv admin note: text overlap with arXiv:1202.6283; and with
arXiv:1106.4730 by other author
Space-time multilevel quadrature methods and their application for cardiac electrophysiology
We present a novel approach which aims at high-performance uncertainty quantification for cardiac electrophysiology simulations. Employing the monodomain equation to model the transmembrane potential inside the cardiac cells, we evaluate the effect of spatially correlated perturbations of the heart fibers on the statistics of the resulting quantities of interest. Our methodology relies on a close integration of multilevel quadrature methods, parallel iterative solvers and space-time finite element discretizations, allowing for a fully parallelized framework in space, time and stochastics. Extensive numerical studies are presented to evaluate convergence rates and to compare the performance of classical Monte Carlo methods such as standard Monte Carlo (MC) and quasi-Monte Carlo (QMC), as well as multilevel strategies, i.e. multilevel Monte Carlo (MLMC) and multilevel quasi-Monte Carlo (MLQMC) on hierarchies of nested meshes. Finally, we employ a recently suggested variant of the multilevel approach for non-nested meshes to deal with a realistic heart geometry
Fast uncertainty quantification of tracer distribution in the brain interstitial fluid with multilevel and quasi Monte Carlo
Efficient uncertainty quantification algorithms are key to understand the
propagation of uncertainty -- from uncertain input parameters to uncertain
output quantities -- in high resolution mathematical models of brain
physiology. Advanced Monte Carlo methods such as quasi Monte Carlo (QMC) and
multilevel Monte Carlo (MLMC) have the potential to dramatically improve upon
standard Monte Carlo (MC) methods, but their applicability and performance in
biomedical applications is underexplored. In this paper, we design and apply
QMC and MLMC methods to quantify uncertainty in a convection-diffusion model of
tracer transport within the brain. We show that QMC outperforms standard MC
simulations when the number of random inputs is small. MLMC considerably
outperforms both QMC and standard MC methods and should therefore be preferred
for brain transport models.Comment: Multilevel Monte Carlo, quasi Monte Carlo, brain simulation, brain
fluids, finite element method, biomedical computing, random fields,
diffusion-convectio
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