Universality of Approximate Message Passing Algorithms

03/23/2020
by   Wei-Kuo Chen, et al.
0

We consider a broad class of Approximate Message Passing (AMP) algorithms defined as a Lipschitzian functional iteration in terms of an n× n random symmetric matrix A. We establish universality in noise for this AMP in the n-limit and validate this behavior in a number of AMPs popularly adapted in compressed sensing, statistical inferences, and optimizations in spin glasses.

READ FULL TEXT

page 1

page 2

page 3

page 4

01/10/2019

A Unified Framework of State Evolution for Message-Passing Algorithms

This paper presents a unified framework to understand the dynamics of me...
11/02/2020

Compressed Sensing with Upscaled Vector Approximate Message Passing

Recently proposed Vector Approximate Message Passing (VAMP) demonstrates...
05/05/2021

A unifying tutorial on Approximate Message Passing

Over the last decade or so, Approximate Message Passing (AMP) algorithms...
10/05/2021

Approximate Message Passing for orthogonally invariant ensembles: Multivariate non-linearities and spectral initialization

We study a class of Approximate Message Passing (AMP) algorithms for sym...
05/14/2021

Divergence Estimation in Message Passing algorithms

Many modern imaging applications can be modeled as compressed sensing li...
12/29/2017

A Unified Bayesian Inference Framework for Generalized Linear Models

In this letter, we present a unified Bayesian inference framework for ge...
08/05/2022

A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models

Approximate message passing (AMP) emerges as an effective iterative para...