Adaptive matrix algebras in unconstrained minimization

Stefano Cipolla, Carmine Di Fiore, Francesco Tudisco, Paolo Zellini

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this paper we study adaptive L(k)QN methods, involving special matrix algebras of low complexity, to solve general (non-structured) unconstrained minimization problems. These methods, which generalize the classical BFGS method, are based on an iterative formula which exploits, at each step, an ad hoc chosen matrix algebra L(k). A global convergence result is obtained under suitable assumptions on f.
LanguageEnglish
Pages544-568
Number of pages25
JournalLinear Algebra and its Applications
Volume471
DOIs
Publication statusPublished - 30 Jan 2015

Fingerprint

Unconstrained Minimization
Matrix Algebra
BFGS Method
Global Convergence
Convergence Results
Minimization Problem
Low Complexity
Generalise

Keywords

  • unconstrained minimisation
  • unconstrained minimization
  • quasi-Newton BFGS method
  • matrix algebras
  • iterative algorithm
  • convergence

Cite this

Cipolla, Stefano ; Di Fiore, Carmine ; Tudisco, Francesco ; Zellini, Paolo. / Adaptive matrix algebras in unconstrained minimization. In: Linear Algebra and its Applications. 2015 ; Vol. 471. pp. 544-568.
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Adaptive matrix algebras in unconstrained minimization. / Cipolla, Stefano; Di Fiore, Carmine; Tudisco, Francesco; Zellini, Paolo.

In: Linear Algebra and its Applications, Vol. 471, 30.01.2015, p. 544-568.

Research output: Contribution to journalArticle

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