Numerical Experience with a Reduced Hessian Methodfor Large Scale Constrained Optimization

Authors:
Lorenz T. Biegler;Jorge Nocedal;Claudia Schmid;David Ternet
Affiliations:
Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213;Department of Electrical and Computer Engineering, Northwestern University, Evanston, Il 60208. nocedal@ece.nwu.edu.;Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213;Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213
Venue:
Computational Optimization and Applications
Year:
2000

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Abstract

The reduced Hessian SQP algorithm presented in Biegleret al. [SIAM J. Optimization, Vol. 5, no. 2, pp. 314–347, 1995.] isdeveloped in this paper into a practical method for large-scaleoptimization. The novelty of the algorithm lies in the incorporationof a correction vector that approximates the cross term Z^TWY_p.This improves the stability and robustness of the algorithm withoutincreasing its computational cost. The paper studies how to implementthe algorithm efficiently, and presents a set of tests illustratingits numerical performance. An analytic example, showing the benefitsof the correction term, is also presented.