Low-Rank Matrix Recovery

Approximate Message Passing for Quantum State Tomography
Meaningful comparison between sets of observations often necessitates alignment or registration between them, and the resulting …
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Linear Convergence of Iteratively Reweighted Least Squares for Nuclear Norm Minimization
Low-rank matrix recovery problems are ubiquitous in many areas of science and engineering. One approach to solve these problems is …
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On the robustness of noise-blind low-rank recovery from rank-one measurements
We prove new results about the robustness of well-known convex noise-blind optimization formulations for the reconstruction of low-rank …
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A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples
We propose an iterative algorithm for low-rank matrix completion that can be interpreted as an iteratively reweighted least squares …
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Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method
We propose an iterative algorithm for low-rank matrix completion that can be interpreted as both an iteratively reweighted least …
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Understanding and Enhancing Data Recovery Algorithms - From Noise-Blind Sparse Recovery to Reweighted Methods for Low-Rank Matrix Optimization
We prove new results about the robustness of noise-blind decoders for the problem of re- constructing a sparse vector from …
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Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery
We propose a new iteratively reweighted least squares (IRLS) algorithm for the recovery of a matrix $X \in \mathbb{C}^{d_1 \times d_2}$ …
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Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery
This is a first conference version of the paper on Harmonic Mean Iteratively Reweighted Least Squares.
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