A Second Order Method for the Linearly Constrained Nonlinear Programming Problem

An algorithm using second derivatives for solving the problem: minimize f(x) subject to Ax − b ≥ 0 is presented. Convergence to a Second-Order Kuhn Tucker Point is proved. If the strict second-order sufficiency conditions hold, the rate of convergence of the algorithm is shown to be superlinear or e...

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Published in:Nonlinear Programming pp. 207 - 243
Main Author: McCORMICK, GARTH P.
Format: Book Chapter
Language:English
Published: United States Elsevier Inc 1970
Elsevier Science & Technology
Subjects:
Applied mathematics
ISBN:9780125970501, 148327246X, 9781483245638, 9781483272467, 1483245632, 0125970501
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Abstract An algorithm using second derivatives for solving the problem: minimize f(x) subject to Ax − b ≥ 0 is presented. Convergence to a Second-Order Kuhn Tucker Point is proved. If the strict second-order sufficiency conditions hold, the rate of convergence of the algorithm is shown to be superlinear or even quadratic with aLipschitz condition on the second derivatives of f(x).
AbstractList An algorithm using second derivatives for solving the problem: minimize f(x) subject to Ax − b ≥ 0 is presented. Convergence to a Second-Order Kuhn Tucker Point is proved. If the strict second-order sufficiency conditions hold, the rate of convergence of the algorithm is shown to be superlinear or even quadratic with aLipschitz condition on the second derivatives of f(x).
Author McCORMICK, GARTH P.
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PublicationSubtitle Proceedings of a Symposium Conducted by the Mathematics Research Center, the University of Wisconsin, Madison, May 4-6 1970
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Snippet An algorithm using second derivatives for solving the problem: minimize f(x) subject to Ax − b ≥ 0 is presented. Convergence to a Second-Order Kuhn Tucker...
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SubjectTerms Applied mathematics
Title A Second Order Method for the Linearly Constrained Nonlinear Programming Problem
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