Trust-region methods for large-scale unconstrained optimization
Philip E. Gill
Department of Mathematics, UCSD
We consider methods for large-scale unconstrained optimization based on finding an approximate solution of a quadratically constrained trust-region subproblem. The solver is based on sequential subspace minimization with a modified barrier "accelerator" direction in the subspace basis.
Tuesday, April 29, 2008
11:00AM AP&M 2402
Center for Computational Mathematics9500 Gilman Dr. #0112La Jolla, CA 92093-0112Tel: (858)534-9056