A REDUCED HESSIAN METHOD FOR LARGE SCALE CONSTRAINED OPTIMIZATION by
نویسندگان
چکیده
We propose a quasi Newton algorithm for solving large optimization problems with nonlinear equality constraints It is designed for problems with few degrees of freedom and is motivated by the need to use sparse matrix factorizations The algorithm incorporates a correction vector that approximates the cross term ZWY pY in order to estimate the curvature in both the range and null spaces of the constraints The algorithm can be considered to be in some sense a practical implementation of an algorithm of Coleman and Conn We give conditions under which local and superlinear convergence is obtained
منابع مشابه
OPTIMIZATION TECHNOLOGY CENTER Argonne National Laboratory and Northwestern University NUMERICAL EXPERIENCE WITH A REDUCED HESSIAN METHOD FOR LARGE SCALE CONSTRAINED OPTIMIZATION by
The reduced Hessian SQP algorithm presented in 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 ZWY pY This improves the stability and robustness of the algorithm without increasing its computational cost The paper studies how to implement the algorithm e...
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