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 | Citation: | 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 |
| Appears in Collections: | Staff Publications |
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.