A Smoothing Newton-Type Algorithm of Stronger Convergence for the Quadratically Constrained Convex Quadratic Programming |
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Authors: | Zheng-Hai Huang Defeng Sun Gongyun Zhao |
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Institution: | (1) Department of Mathematics, School of Science, Tianjin University, Tianjin, 300072, P.R. China;(2) Department of Mathematics, National University of Singapore, 2 Science Drive 2, Singapore, 117543, Republic of Singapore |
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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.
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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Keywords: | smoothing Newton method global convergence superlinear convergence |
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