A Smoothing Newton-Type Algorithm of Stronger Convergence for the Quadratically Constrained Convex Quadratic Programming

In this paper we propose a smoothing Newton-type algorithm for the problem of minimizing a convex quadratic function subject to finitely many convex quadratic inequality constraints. The algorithm is shown to converge globally and possess stronger local superlinear convergence. Preliminary numerical...

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Bibliographic Details
Published inComputational optimization and applications Vol. 35; no. 2; pp. 199 - 237
Main Authors Huang, Zheng-Hai, Sun, Defeng, Zhao, Gongyun
Format Journal Article
LanguageEnglish
Published New York Springer Nature B.V 01.10.2006
Subjects
Algorithms
Applied mathematics
Convex analysis
Linear programming
Mathematical programming
Optimization
Quadratic programming
Studies
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ISSN0926-6003
1573-2894
1573-2894
DOI10.1007/s10589-006-6512-7

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Summary:In this paper we propose a smoothing Newton-type algorithm for the problem of minimizing a convex quadratic function subject to finitely many convex quadratic inequality constraints. The algorithm is shown to converge globally and possess stronger local superlinear convergence. Preliminary numerical results are also reported.
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ISSN:0926-6003
1573-2894
1573-2894
DOI:10.1007/s10589-006-6512-7