Adaptive matrix algebras in unconstrained minimization

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

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)544-568
Number of pages25
JournalLinear Algebra and its Applications
Publication statusPublished - 30 Jan 2015


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


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