a new path-following primal-dual augmented lagrangian method is proposed for solving nonlinear equality constrained optimization problems (nep). at each iteration, a newton-like method is used to solve a perturbed optimality condition that defines a penalty trajectory parameterized by both the penalty parameter and the estimated lagrange multipliers. we show that this method is globally convergent and has a quadratic convergence rate in the limit. finally, numerical experiments on problems from the cutest test collection are are used to support the theoretical analysis.