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dc.contributor.authorRosen, J.B.en_US
dc.contributor.authorKreuser, J.L.en_US
dc.description.abstractAn algorithm for the nonlinearly constrained optimization problem is presented. The algorithm consists of a sequence of major iterations generated by linearizing each nonlinear constraint about the current point, and adding to the objective function a linear penalty for each nonlinear constraint. The resulting function is essentially the Lagrangian. A Kantorovich-type theorem is given, showing quadratic convergence in terms of major iterations. This theorem insures quadratic convergence if the starting point (or any subsequent point) satisfies a condition which can be tested using computable bounds on the objective and constraint functions.en_US
dc.publisherUniversity of Wisconsin-Madison Department of Computer Sciencesen_US
dc.titleA Quadratically Convergent Lagrangian Algorithm for Nonlinear Constraintsen_US
dc.typeTechnical Reporten_US

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  • CS Technical Reports
    Technical Reports Archive for the Department of Computer Sciences at the University of Wisconsin-Madison

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