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Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/7140
Title: A Quasi-Newton $L_{2}$-Penalty Method for Minimization Subject toNonlinear Equality Constraints
Authors: Coleman, Thomas F.
Yuan, Wei
Keywords: computer science
technical report
Issue Date: Mar-1995
Publisher: Cornell University
Citation: http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR95-1481
Abstract: We present a modified $L_{2}$ penalty function method for equality constrained optimization problems. The pivotal feature of our algorithm is that at every iterate we invoke a special change of variables to improve the ability of the algorithm to follow the constraint level sets. This change of variables gives rise to a suitable block diagonal approximation to the Hessian which is then used to construct a quasi-Newton method. We show that the complete algorithm is globally convergent with a local Q-superlinearly convergence rate. Preliminary computational results are given for a few problems.
URI: http://hdl.handle.net/1813/7140
Appears in Collections:Computer Science Technical Reports

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