  The BFGS quasi-Newton methodology, popular for smooth minimization, has also
proved surprisingly effective in nonsmooth optimization. Through a variety of
simple examples and computational experiments, we explore how the BFGS matrix
update improves the local metric associated with a convex function even in the
absence of smoothness and without using a line search. We compare the behavior
of the BFGS and Shor r-algorithm updates.
