Seminar series
Date
Tue, 20 Oct 2020
Time
12:45 -
13:30
Speaker
Zhen Shao
Organisation
Oxford University
We propose a subspace Gauss-Newton method for nonlinear least squares problems that builds a sketch of the Jacobian on each iteration. We provide global rates of convergence for regularization and trust-region variants, both in expectation and as a tail bound, for diverse choices of the sketching matrix that are suitable for dense and sparse problems. We also have encouraging computational results on machine learning problems.