“…The AMP algorithm and machinery has been successfully applied to a variety of problems beyond compressed sensing, including but not limited to robust M-estimators (Donoho and Montanari, 2016), SLOPE (Bu et al, 2020), low-rank matrix estimation and PCA (Rangan and Fletcher, 2012;Montanari and Venkataramanan, 2021;Fan, 2020;Zhong et al, 2021), stochastic block models (Deshpande et al, 2015), phase retrieval (Ma et al, 2018), phase synchronization (Celentano et al, 2021), and generalized linear models (Rangan, 2011;Barbier et al, 2019). See Feng et al (2021) for an accessible introduction of this machinery and its applications. Moreover, a dominant fraction of the AMP works focused on high-dimensional asymptotics (so that the problem dimension tends to infinity first before the number of iterations), except for Rush and Venkataramanan (2018) that derived finite-sample guarantees allowing the number of iterations to grow up to O(log n/ log log n).…”