A Quasi-Newton $L_{2}$-Penalty Method for Minimization Subject toNonlinear Equality Constraints

We present a modified $L_{2}$ 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 computational results are given for a few problems