Traffic distributions of certain deterministic matrices determine the limiting dynamics of general first-order methods, and a new unified approximate message passing algorithm is introduced whose state remains conditionally Gaussian for a broad class of input matrices.
State evolution for general approximate message passing algorithms, with applications to spatial coupling.Information and Inference: A Journal of the IMA, 2(2):115–144
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Universality of first-order methods on random and deterministic matrices
Traffic distributions of certain deterministic matrices determine the limiting dynamics of general first-order methods, and a new unified approximate message passing algorithm is introduced whose state remains conditionally Gaussian for a broad class of input matrices.