André Uschmajew (MPI MIS, Leipzig + Leipzig University): Gradient methods for sparse low-rank matrix recovery
Ort: MPI für Mathematik in den Naturwissenschaften Leipzig, , Videobroadcast
Video broadcast: Nonlinear Algebra Seminar Online (NASO) 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.
Beginn: Nov. 24, 2020, 5 p.m.
Ende: Nov. 24, 2020, 5:45 p.m.