Compressive phase retrieval via generalized approximate message passing

By Philip Schniter, Sundeep Rangan. Allerton Conference on Communication, Control, and Computing, 2012

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Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution (2017, background): “A small sample of other nonconvexphase retrievalmethods include [6,10,22,36,43,47,92,98,100,109,122], which are beyond the scope of this paper.”
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems (2015, unknown): “Here and throughout, we let A : Rn×n 7→ Rm be the linear map M ∈ Rn×n 7→ A (M) := { a>i Mai } 1≤i≤m and A the design matrix A := [a1, · · · ,am]>.”
Phase Retrieval via Wirtinger Flow: Theory and Algorithms (2014, background): “In [63], the authors demonstrate a favorable runtime for an algorithm of this nature.”
Statistical physics of inference: thresholds and algorithms (2015, methodology): “[212] Philip Schniter and Sundeep Rangan.”
AMP-Inspired Deep Networks for Sparse Linear Inverse Problems (2016, unknown): “Doing so would facilitate the application of AMP-inspired deep networks to problems such as phase retrieval [52] and quantized compressive sensing [53].”
Orthogonal AMP (2016, background): “These distinguished features have attracted wide research interests on AMP for various signal processing [15], [16] and communication [17]–[21] applications .”