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

Authors:
Zheng-Hai Huang;Defeng Sun;Gongyun Zhao
Affiliations:
Department of Mathematics, School of Science, Tianjin University, Tianjin, P.R. China 300072;Department of Mathematics, National University of Singapore, Singapore, Republic of Singapore 117543;Department of Mathematics, National University of Singapore, Singapore, Republic of Singapore 117543
Venue:
Computational Optimization and Applications
Year:
2006

Citing 24
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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.