Date: Nov 3rd, Thursday
Time: 3:00 pm
Place: Little 339 (the Atrium)
Speaker: Hongchao Zhang
title: Self-Adaptive Inexact Proximal Point Methods


We discuss a class of self-adaptive proximal point methods suitable
for degenerate optimization problems where multiple minimizers may exist,
or where the Hessian may be singular at a local minimizer.
Depending on choosing the regularization parameter in a particular form,
we obtain convergence to the set of minimizers that is linear
,superlinear, or at least quadratic.
Two different acceptance criteria for an approximate solution to the
proximal problem are analyzed.