Numerical Experience with a Reduced Hessianmethod for Large Scaleconstrained

The reduced Hessian SQP algorithm presented in 2] is developed in this paper into a practical method for large-scale optimization. The novelty of the algorithm lies in the incorporation of a correction vector that approximates the cross term Z T WY p Y. This improves the stability and robustness of the algorithm without increasing its computational cost. The paper studies how to implement the algorithm eeciently, and presents a set of tests illustrating its numerical performance. An analytic example , showing the beneets of the correction term, is also presented.