836 research outputs found
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
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
Scheduling Massively Parallel Multigrid for Multilevel Monte Carlo Methods
The computational complexity of naive, sampling-based uncertainty quantification for 3D partial differential equations is extremely high. Multilevel approaches, such as multilevel Monte Carlo (MLMC), can reduce the complexity significantly when they are combined with a fast multigrid solver, but to exploit them fully in a parallel environment, sophisticated scheduling strategies are needed. We optimize the concurrent execution across the three layers of the MLMC method: parallelization across levels, across samples, and across the spatial grid. In a series of numerical tests, the influence on the overall performance of the “scalability window” of the multigrid solver (i.e., the range of processor numbers over which good parallel efficiency can be maintained) is illustrated. Different homogeneous and heterogeneous scheduling strategies are proposed and discussed. Finally, large 3D scaling experiments are carried out, including adaptivity
Adaptive Multilevel Monte Carlo Methods for Stochastic Variational Inequalities
While multilevel Monte Carlo (MLMC) methods for the numerical approximation of partial differential equations with random coefficients enjoy great popularity, combinations with spatial adaptivity seem to be rare. We present an adaptive MLMC finite element approach based on deterministic adaptive mesh refinement for the arising “pathwise” problems and outline a convergence theory in terms of desired accuracy and required computational cost. Our theoretical and heuristic reasoning together with the efficiency of our new approach are confirmed by numerical experiments
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