6 papers
Information-Theoretic Limits for Rectangular Spiked Matrix Models with Rotationally Invariant Noise
Qun Chen, Rudy Skerk, Jean Barbier +3
We study the information-theoretic limits of inference in rectangular rank-one spiked matrix models with additive rotationally invariant noise, thereby going beyond the standard as…
Orthogonal Approximate Message Passing Algorithms for Rectangular Spiked Matrix Models with Rotationally Invariant Noise
Haohua Chen, Songbin Liu, Junjie Ma
We propose an orthogonal approximate message passing (OAMP) algorithm for signal estimation in the rectangular spiked matrix model with general rotationally invariant (RI) noise. W…
Orthogonal Approximate Message Passing with Optimal Spectral Initializations for Rectangular Spiked Matrix Models
Haohua Chen, Songbin Liu, Junjie Ma
We propose an orthogonal approximate message passing (OAMP) algorithm for signal estimation in the rectangular spiked matrix model with general rotationally invariant (RI) noise. W…
Unifying AMP Algorithms for Rotationally-Invariant Models
Songbin Liu, Junjie Ma
This paper presents a unified framework for constructing Approximate Message Passing (AMP) algorithms for rotationally-invariant models. By employing a general iterative algorithm…
Optimality of Approximate Message Passing Algorithms for Spiked Matrix Models with Rotationally Invariant Noise
Rishabh Dudeja, Songbin Liu, Junjie Ma
We study the problem of estimating a rank one signal matrix from an observed matrix generated by corrupting the signal with additive rotationally invariant noise. We develop a new…
Convolution quadratures based on block generalized Adams methods
Ling Liu, Junjie Ma
This paper studies a family of convolution quadratures, a numerical technique for efficient evaluation of convolution integrals. We employ the block generalized Adams method to dis…