A global convergence theory for general trust-region-based algorithms for equality constrained optimization

This work presents a global convergence theory for a broad class of trust-region algorithms for the smooth nonlinear programming problem with equality constraints. The main result generalizes Powell's 1975 result for unconstrained trust-region algorithms. The trial step is characterized by very mild conditions on its normal and tangential components. The normal component need not be computed accurately. The theory requires a quasi-normal component to satisfy a fraction of Cauchy decrease condition on the quadratic model of the linearized constraints. The tangential component then must satisfy a fraction of Cauchy decrease condition on a quadratic model of the Lagrangian function in the translated tangent space of the constraints determined by the quasi-normal component. Estimates of the Lagrange multipliers and the Hessians are assumed only to be bounded. The other main characteristic of this class of algorithms is that the step is evaluated by using the augmented Lagrangian as a merit function with the penalty parameter updated using the El-Alem scheme. The properties of the step and the way that the penalty parameter is chosen are sufficient to establish global convergence. As an example, an algorithm is presented that can be viewed as a generalization of the Steihaug-Toint dogleg algorithm for the unconstrained case. It is based on a quadratic programming algorithm that uses a step in a quasi-normal direction to the tangent space of the constraints and then takes feasible conjugate reduced-gradient steps to solve the reduced quadratic program. This algorithm should cope quite well with large problems for which effective preconditioners are known.