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

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Abstract

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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Authors and Affiliations

  1. Department of Mathematics, School of Science, Tianjin University, Tianjin, 300072, P.R. China

    Zheng-Hai Huang

  2. Department of Mathematics, National University of Singapore, 2 Science Drive 2, Singapore, 117543, Republic of Singapore

    Defeng Sun & Gongyun Zhao

Authors
  1. Zheng-Hai Huang
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  2. Defeng Sun
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  3. Gongyun Zhao
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Correspondence to Zheng-Hai Huang.

Additional information

Mathematics Subject Classification (1991): 90C33, 65K10

This author’s work was also partially supported by the Scientific Research Foundation of Tianjin University for the Returned Overseas Chinese Scholars and the Scientific Research Foundation of Liu Hui Center for Applied Mathematics, Nankai University-Tianjin University.

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Huang, ZH., Sun, D. & Zhao, G. A Smoothing Newton-Type Algorithm of Stronger Convergence for the Quadratically Constrained Convex Quadratic Programming. Comput Optim Applic 35, 199–237 (2006). https://doi.org/10.1007/s10589-006-6512-7

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  • DOI: https://doi.org/10.1007/s10589-006-6512-7

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