  The generalized approximate message passing (GAMP) algorithm is an efficient
method of MAP or approximate-MMSE estimation of $x$ observed from a noisy
version of the transform coefficients $z = Ax$. In fact, for large zero-mean
i.i.d sub-Gaussian $A$, GAMP is characterized by a state evolution whose fixed
points, when unique, are optimal. For generic $A$, however, GAMP may diverge.
In this paper, we propose adaptive damping and mean-removal strategies that aim
to prevent divergence. Numerical results demonstrate significantly enhanced
robustness to non-zero-mean, rank-deficient, column-correlated, and
ill-conditioned $A$.
