A non-asymptotic framework for approximate message passing

Thu April 6th 2023, 4:30pm
History 034
Yuting Wei, Wharton/U Penn

Approximate message passing (AMP) emerges as an effective iterative paradigm for solving high-dimensional statistical problems. However, prior AMP theory, which focused mostly on high-dimensional asymptotics, fell short of predicting the AMP dynamics when the number of iterations surpasses o(log n / log log n) (with n the problem dimension). To address this inadequacy, this talk introduces a non-asymptotic framework towards understanding AMP. Built upon a new decomposition of AMP updates in conjunction with well-controlled residual terms, we lay out an analysis recipe to characterize the finite-sample convergence of AMP up to O(n / polylog(n)) iterations. We will discuss concrete consequences of the proposed analysis recipe in the Z2 synchronization problem; more specifically, our theory provides the first non-asymptotic characterization of AMP in this model without requiring either an informative initialization (e.g., spectral initialization) or a subsequent refinement stage (as conjectured recently by Celentano et al.). Time permitting, we will also discuss the non-asymptotic behavior of AMP in sparse PCA (in the spiked Wigner model).