  Approximate message passing is an iterative algorithm for compressed sensing
and related applications. A solid theory about the performance and convergence
of the algorithm exists for measurement matrices having iid entries of zero
mean. However, it was observed by several authors that for more general
matrices the algorithm often encounters convergence problems. In this paper we
identify the reason of the non-convergence for measurement matrices with iid
entries and non-zero mean in the context of Bayes optimal inference. Finally we
demonstrate numerically that when the iterative update is changed from parallel
to sequential the convergence is restored.
