Abstract for the talk on 24.11.2020 (17:00 h)Nonlinear Algebra Seminar Online (NASO)
André Uschmajew (MPI MIS, Leipzig + Leipzig University)
Gradient methods for sparse low-rank matrix recovery
See the video of this talk.
The problem of recovering a row or column sparse low rank matrix from linear measurements arises for instance in sparse blind deconvolution. The ideal goal is to ensure recovery using only a minimal number of measurements with respect to the joint low-rank and sparsity constraint. We consider gradient based optimization methods for this task that combine ideas of hard and soft thresholding with Riemannian optimization. This is joint work with Henrik Eisenmann, Felix Krahmer and Max Pfeffer.