Structured Low-Rank Optimization

We consider the nonlinear inverse problem of learning a transition operator A from partial observations at T different times, in the form of sparse observations of entries of the powers $A,A^2,...,A^T$. We address the nonlinearity of this …

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 …

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 …