Global and superlinear convergence of inexact sequential quadratically constrained quadratic programming method for convex programming

Atsushi Kato, Yasushi Narushima, Hiroshi Yabe

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper is concerned with a sequential quadratically constrained quadratic programming (SQCQP) method for convex programming. The SQCQP method solves, at each iteration, a quadratically constrained quadratic programming subproblem whose objective function and constraints are quadratic approximations to the objective function and constraints of the original problem, respectively. We propose an inexact SQCQP method which solves inexactly the subproblem and prove its global and superlinear convergence properties.

Original languageEnglish
Pages (from-to)609-629
Number of pages21
JournalPacific Journal of Optimization
Volume8
Issue number3
Publication statusPublished - 2012 Jul 1
Externally publishedYes

Fingerprint

Superlinear Convergence
Convex optimization
Convex Programming
Quadratic programming
Quadratic Programming
Global Convergence
Objective function
Quadratic Approximation
Convergence Properties
Iteration

Keywords

  • Convex programming
  • Global convergence
  • Sequential quadratically constrained quadratic programming method
  • Superlinear convergence

ASJC Scopus subject areas

  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics

Cite this

Global and superlinear convergence of inexact sequential quadratically constrained quadratic programming method for convex programming. / Kato, Atsushi; Narushima, Yasushi; Yabe, Hiroshi.

In: Pacific Journal of Optimization, Vol. 8, No. 3, 01.07.2012, p. 609-629.

Research output: Contribution to journalArticle

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