LGRT 1681
The calculation of steady-states of systems which are both non-reversible and metastable presents a major computational challenge. Here we present work on a class of methods that seek to overcome this challenge by stratifying the underlying space, that it, breaking it up into parts and sampling on each part. Iterative Aggregation Disaggregation (IAD) is a model of such methods, and we present a result showing why it can accelerate convergence to the steady state for many systems.