Dual techniques for constrained optimization. II

For pt.I see J. Optim. Theory Appl., vol.55, p.37-71 (1987). An algorithm for constrained optimization that combines an unconstrained minimization scheme like the conjugate gradient method, an augmented Lagrangian, and multiplier updates to obtain global quadratic convergence was presented in part I. Issues related to the numerical implementation of the algorithm are considered here. The convergence theory is extended to handle the rigid constraints that are not violated during the iterations. A strategy is developed for balancing the error associated with constraint violation with the error associated with optimality. Various numerical linear algebra techniques required for the efficient implementation of the algorithm are also developed, and the convergence properties of the algorithm are illustrated using some standard test problems