Information-based complexity (IBC) deals with the computational complexity of continuous
problems for which available information is partial, priced and noisy. IBC provides a
methodological background for proving the curse of dimensionality as well as provides
various ways of vanquishing this curse.
Stochastic computation deals with computational problems that arise in
probabilistic models or can be efficiently solved by randomized algorithms.
Using IBC background, the complexity of stochastic ordinary (SDE) and
partial differential (SPDE) equations have been studied.
Topics covered in the workshop will include: adaptive and nonlinear
approximation for SPDEs, infinite-dimensional problems, inverse and ill-
posed problems, quasi-Monte Carlo methods, PDEs with random coefficients,
sparse/Smolyak grids, stochastic multi-level algorithms, SDEs and SPDEs
with nonstandard coefficients, tractability of multivariate problems.
This workshop will bring together researchers from these different fields. The goal is to explore connections,
learn and share techniques, and build bridges.