In this paper we develop the large deviations principle and a rigorous
mathematical framework for asymptotically efficient importance sampling schemes
for general, fully dependent systems of stochastic differential equations of
slow and fast motion with small noise in the slow component. We assume
periodicity with respect to the fast component. Depending on the interaction of
the fast scale with the smallness of the noise, we get different behavior. We
examine how one range of interaction differs from the other one both for the
large deviations and for the importance sampling. We use the large deviations
results to identify asymptotically optimal importance sampling schemes in each
case. Standard Monte Carlo schemes perform poorly in the small noise limit. In
the presence of multiscale aspects one faces additional difficulties and
straightforward adaptation of importance sampling schemes for standard small
noise diffusions will not produce efficient schemes. It turns out that one has
to consider the so called cell problem from the homogenization theory for
Hamilton-Jacobi-Bellman equations in order to guarantee asymptotic optimality.
We use stochastic control arguments.Comment: More detailed proofs. Differences from the published version are
editorial and typographica