"Numerical Experience with a Reduced Hessian Method for Large-Scale Constrained Optimization"
L. Biegler, J. Nocedal, C. Schmid, D. Ternet.
Computational Optimization and Applications, Vol. 15, No. 1 (2000)

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 a defined cross term. This improves the stability and robustness of the algorithm without increasing its computational cost. The paper studies how to implement the algorithm efficiently, and presents a set of tests illustrating its numerical performance. An analytic example, showing the benefits of the correction term, is also presented.
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