A Unified Framework of State Evolution for Message-Passing Algorithms

This paper presents a unified framework to understand the dynamics of message-passing algorithms in compressed sensing. State evolution is rigorously analyzed for a general error model that contains the error model of approximate message-passing (AMP), as well as that of orthogonal AMP. As a byproduct, AMP is proved to converge asymptotically if the sensing matrix is orthogonally invariant and if the moment sequence of its asymptotic singular-value distribution coincide with that of the Marčhenko-Pastur distribution up to the order that is at most twice as large as the maximum number of iterations.