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 publicationJournal articleAcademic researchpeer-review

33 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 languageEnglish
Pages (from-to)199-237
Number of pages39
JournalComputational Optimization and Applications
Volume35
Issue number2
DOIs
Publication statusPublished - 1 Oct 2006
Externally publishedYes

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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