A Quasi-Newton L2-Penalty Method for Minimization Subject to Nonlinear Constraints

Abstract

We present a modified L2 penalty function method for equality constrained optimization problems. The pivotal feature of our algorithm is that at every iterate we invoke a special change of variables to improve the ability of the algorithm to follow the constraint level sets. This change of variables gives rise to a suitable block diagonal approximation to the Hessian which is then used to construct a quasi-Newton method. We show that the complete algorithm is globally convergent with a local Q-superlinearly convergence rate. Preliminary results are given for a few problems.