We present globally convergent multigrid methods for the nonsymmetric
obstacle problems as arising from the discretization of Black–Scholes models of
American options with local volatilities and discrete data. No tuning or regularization
parameters occur. Our approach relies on symmetrization by transformation
and data recovery by superconvergence.
We shortly review the uncoupling-coupling method, a Markov chain
Monte Carlo based approach to compute statistical properties of systems like
medium-sized biomolecules. This technique has recently been proposed for the efficient computation of biomolecular conformations. One crucial step of UC is the
decomposition of reversible nearly uncoupled Markov chains into rapidly mixing
subchains. We show how the underlying scheme of uncoupling-coupling can also be
applied to stochastic differential equations where it can be translated into a domain
decomposition technique for partial differential equations.