Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10589-006-6512-7
Title: A smoothing newton-type algorithm of stronger convergence for the quadratically constrained convex quadratic programming
Authors: Huang, Z.-H.
Sun, D. 
Zhao, G. 
Keywords: Global convergence
Smoothing Newton method
Superlinear convergence
Issue Date: Oct-2006
Source: Huang, Z.-H., Sun, D., Zhao, G. (2006-10). A smoothing newton-type algorithm of stronger convergence for the quadratically constrained convex quadratic programming. Computational Optimization and Applications 35 (2) : 199-237. ScholarBank@NUS Repository. https://doi.org/10.1007/s10589-006-6512-7
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. © 2006 Springer Science + Business Media, LLC.
Source Title: Computational Optimization and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/102765
ISSN: 09266003
DOI: 10.1007/s10589-006-6512-7
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