Approximate Message Passing (AMP) algorithms are non-linear power iterations originally arising from the context of compressed sensing. In this talk, I will introduce a Lipschitzian functional iteration, as a generalization of the AMP algorithms, and discuss its universality in disorder. In addition, I will explain how our results imply universality in a number of AMPs popularly adapted in Bayesian inferences and optimizations in spin glasses.
This is based on a joint work with Wai-Kit Lam.
Joint with Applied Mathematics Seminar