We present a sequential quadratic programming (SQP) algorithm for nonlinear optimization. We give a brief overview of SQP methods in general and then describe an active-set method based on inertia control for solving the convex quadratic subproblems. We also discuss the motivation behind this algorithm as well as its applications.
Tuesday, March 3, 2009
11:00AM AP&M 2402
Center for Computational Mathematics9500 Gilman Dr. #0112La Jolla, CA 92093-0112Tel: (858)534-9813