**Raul Tempone
Division of Mathematics & Computer, Electrical and Mathematical Sciences & Engineering
King Abdullah University of Science and Technology
Thuwal
Saudi Arabia
**

We propose and analyze a novel Multi-Index Monte Carlo (MIMC) method
for weak approximation of stochastic models that are described in
terms of differential equations either driven by random measures or
with random coefficients. The MIMC method is both a stochastic
version of the combination technique introduced by Zenger, Griebel
and collaborators and an extension of the Multilevel Monte Carlo
(MLMC) method first described by Heinrich and Giles. Inspired by
Giles's seminal work, we use in MIMC high-order mixed differences
instead of using first-order differences as in MLMC to reduce the
variance of the hierarchical differences dramatically. This in turn
yields new and improved complexity results, which are natural
generalizations of Giles's MLMC analysis and which increase the
domain of the problem parameters for which we achieve the optimal
convergence, O(\tol^{-2}). Moreover, in
MIMC, the rate of increase of required memory with respect to
$\tol$ is independent of the number of directions up to a
logarithmic term which allows far more accurate solutions to be
calculated for higher dimensions than what is possible when using
MLMC.

We motivate the setting of MIMC by first focusing on a simple full
tensor index set. We then propose a systematic construction of
optimal sets of indices for MIMC based on properly defined profits
that in turn depend on the average cost per sample and the
corresponding weak error and variance. Under standard assumptions on
the convergence rates of the weak error, variance and work per
sample, the optimal index set turns out to be the total degree (TD)
type. In some cases, using optimal index sets, MIMC achieves a
better rate for the computational complexity than the corresponding
rate when using full tensor index sets. We also show the asymptotic
normality of the statistical error in the resulting MIMC estimator
and justify in this way our error estimate, which allows
both the required accuracy and the confidence level in our
computational results to be prescribed. Finally, we include
numerical experiments involving a partial differential equation
posed in three spatial dimensions and with random coefficients to
substantiate the analysis and illustrate the corresponding
computational savings of MIMC.

Bibliography:

Abdul-Lateef Haji-Ali, Fabio Nobile, Raul Tempone, Multi Index Monte Carlo: When Sparsity Meets Sampling, accepted for publication in Numerische Mathematik, May 2015