In this paper we study adaptive L(k)QNmethods, 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 hocchosen matrix algebra L(k). A global convergence result is obtained under suitable assumptions on f.

Cipolla, S., DI FIORE, C., Tudisco, F., Zellini, P. (2015). Adaptive matrix algebras in unconstrained minimization. LINEAR ALGEBRA AND ITS APPLICATIONS, 471, 544-568 [10.1016/j.laa.2015.01.010].

Adaptive matrix algebras in unconstrained minimization

DI FIORE, CARMINE;ZELLINI, PAOLO
2015-01-01

Abstract

In this paper we study adaptive L(k)QNmethods, 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 hocchosen matrix algebra L(k). A global convergence result is obtained under suitable assumptions on f.
2015
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore MAT/08 - ANALISI NUMERICA
English
Unconstrained minimization; Quasi-Newton methods; Matrix algebras; Iterative procedures
Cipolla, S., DI FIORE, C., Tudisco, F., Zellini, P. (2015). Adaptive matrix algebras in unconstrained minimization. LINEAR ALGEBRA AND ITS APPLICATIONS, 471, 544-568 [10.1016/j.laa.2015.01.010].
Cipolla, S; DI FIORE, C; Tudisco, F; Zellini, P
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/120957
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