Numerical Experience with a Reduced Hessian Method
for Large Scale Optimization
L. Biegler, J. Nocedal, C. Schmid and D. Ternet
The reduced Hessian SQP algorithm analyzed by Biegler,
Nocedal and Schmid 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 ZWYp_y. 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.
Report
OTC 97/06 Optimization Technology Center, July 1997
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