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.