0000000000856978
AUTHOR
Napsu Karmitsa
Test problems for large-scale nonsmooth minimization
Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of hundreds or thousands of variables with various constraints. However, there exist only few large-scale academic test problems for nonsmooth case and there is no established practice for testing solvers for large-scale nonsmooth optimization. For this reason, we now collect the nonsmooth test problems used in our previous numerical experiments and also give some new problems. Namely, we give problems for unconstrained, bound constrained, and inequality constrained nonsmooth minimization.
Limited memory bundle algorithm for inequality constrained nondifferentiable optimization
Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of hundreds or thousands of variables with various constraints. In this paper, we describe a new efficient adaptive limited memory interior point bundle method for large, possible nonconvex, nonsmooth inequality constrained optimization. The method is a hybrid of the nonsmooth variable metric bundle method and the smooth limited memory variable metric method, and the constraint handling is based on the primal-dual feasible direction interior point approach. The preliminary numerical experiments to be presented confirm the effectiveness of the method.