Iteratively Reweighted Least Squares

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 iteratively reweighted least-squares (IRLS) to solve the underlying non-smooth problem. In the convex case, $p=1$, we prove …

Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate

The recovery of sparse data is at the core of many applications in machine learning and signal processing. While such problems can be tackled using $\ell_1$-regularization as in the LASSO estimator and in the Basis Pursuit approach, specialized …

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 (IRLS) algorithm, a saddle-escaping smoothing Newton method or a variable metric proximal gradient method applied to a …

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 squares (IRLS) algorithm and a saddle-escaping smoothing Newton method applied to a non-convex rank surrogate objective. …

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 underdetermined linear measurements. Our results imply provable robustness of equality-constrained l1-minimization for random …

Completion of Structured Low-Rank Matrices via Iteratively Reweighted Least Squares

We propose a new Iteratively Reweighted Least Squares (IRLS) algorithm for the problem of completing a low-rank matrix that is linearly structured, e.g., that possesses a Hankel, Toeplitz or block-Hankel/Toeplitz structures, which is of relevance for …

Denoising and Completion of Structured Low-Rank Matrices via Iteratively Reweighted Least Squares

We propose a new Iteratively Reweighted Least Squares (IRLS) algorithm for the problem of completing or denoising low-rank matrices that are structured, e.g., that possess a Hankel, Toeplitz or block-Hankel/Toeplitz structure. The algorithm optimizes …

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 $X \in \mathbb{C}^{d_1 \times d_2}$ of rank $r \ll \min(d_1,d_2)$ from incomplete linear observations, solv- ing a sequence of low complexity linear …

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.