Superlinear Convergence

Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares
We propose a new algorithm for the problem of recovering data that adheres to multiple, heterogeneous low-dimensional structures from …
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Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression
We advance both the theory and practice of robust $\ell_p$-quasinorm regression for $p \in (0,1]$ by using novel variants of …
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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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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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