nonlinear constrained optimization problem can be effectively solved by minimizing a sequence of unconstrained or linearly constrained subproblems, where the augmented lagrangian function plays a vital role. this talk introduces a generalized hestenes-powell augmented lagrangian function, which can be seen as a continuum of many well-known methods as specific cases. a new primal dual sequential quadratic programming (pdsqp) method will be given for minimizing the given augmented lagrangian