A smoothing newton-type algorithm of stronger convergence for the quadratically constrained convex quadratic programming

Z.-H. Huang; Defeng Sun; G. Zhao

doi:10.1007/s10589-006-6512-7

A smoothing newton-type algorithm of stronger convergence for the quadratically constrained convex quadratic programming

Z.-H. Huang, Defeng Sun, G. Zhao

Research output: Journal article publication › Journal article › Academic research › peer-review

36 Citations (Scopus)

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.

Original language	English
Pages (from-to)	199-237
Number of pages	39
Journal	Computational Optimization and Applications
Volume	35
Issue number	2
DOIs	https://doi.org/10.1007/s10589-006-6512-7
Publication status	Published - 1 Oct 2006
Externally published	Yes

Keywords

Global convergence
Smoothing Newton method
Superlinear convergence

ASJC Scopus subject areas

Applied Mathematics
Control and Optimization
Management Science and Operations Research
Computational Mathematics

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10.1007/s10589-006-6512-7

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KW - Smoothing Newton method

KW - Superlinear convergence

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A smoothing newton-type algorithm of stronger convergence for the quadratically constrained convex quadratic programming

Abstract

Keywords

ASJC Scopus subject areas

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