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Finite Sample Analysis of Approximate Message Passing Algorithms

2018
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IEEE Transactions on Information Theory
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Approximate message passing (AMP) refers to a class of efficient algorithms for statistical estimation in high-dimensional problems such as compressed sensing and low-rank matrix estimation. This paper analyzes the performance of AMP in the regime where the problem dimension is large but finite. For concreteness, we consider the setting of high-dimensional regression, where the goal is to estimate a high-dimensional vector $\beta_0$ from a noisy measurement $y=A \beta_0 + w$. AMP is a

doi:10.1109/tit.2018.2816681
fatcat:juak6g2p3nctvhewsw27ng4bea