dc.contributor.author Coleman, Thomas F. en_US dc.contributor.author Yuan, Wei en_US dc.date.accessioned 2007-04-23T18:00:20Z dc.date.available 2007-04-23T18:00:20Z dc.date.issued 1995-03 en_US dc.identifier.citation http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR95-1481 en_US dc.identifier.uri https://hdl.handle.net/1813/7140 dc.description.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. en_US dc.format.extent 340118 bytes dc.format.extent 471996 bytes dc.format.mimetype application/pdf dc.format.mimetype application/postscript dc.language.iso en_US en_US dc.publisher Cornell University en_US dc.subject computer science en_US dc.subject technical report en_US dc.title A Quasi-Newton $L_{2}$-Penalty Method for Minimization Subject toNonlinear Equality Constraints en_US dc.type technical report en_US
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