  We consider a broad class of Approximate Message Passing (AMP) algorithms
defined as a Lipschitzian functional iteration in terms of an $n\times n$
random symmetric matrix $A$. We establish universality in noise for this AMP in
the $n$-limit and validate this behavior in a number of AMPs popularly adapted
in compressed sensing, statistical inferences, and optimizations in spin
glasses.
