# Structured Low-Rank Optimization

## Learning Transition Operators From Sparse Space-Time Samples

We consider the nonlinear inverse problem of learning a transition operator A from partial observations at different times, in particular from sparse observations of entries of its powers $A,A^2,...,A^T$. This Spatio-Temporal Transition Operator …

## 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 …