Abstract
This paper studies the convergence properties of algorithms belonging to the class of self-scaling (SS) quasi-Newton methods for unconstrained optimization. This class depends on two parameters, say θk and τk, for which the choice τk = 1 gives the Broyden family of unscaled methods, where θk = 1 corresponds to the well known DFP method. We propose simple conditions on these parameters that give rise to global convergence with inexact line searches, for convex objective functions. The q-superlinear convergence is achieved if further restrictions on the scaling parameter are introduced. These convergence results are an extension of the known results for the unscaled methods. Because the scaling parameter is heavily restricted, we consider a subclass of SS methods which satisfies the required conditions. Although convergence for the unscaled methods with θk≥1 is still an open question, we show that the global and superlinear convergence for SS methods is possible and present, in particular, a new SS-DFP method.
| Original language | English |
|---|---|
| Pages (from-to) | 191-203 |
| Number of pages | 13 |
| Journal | Computational Optimization and Applications |
| Volume | 9 |
| Issue number | 2 |
| Publication status | Published - Feb 1998 |
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ASJC Scopus subject areas
- Management Science and Operations Research
- Applied Mathematics
- Computational Mathematics
- Control and Optimization
Cite this
Global and superlinear convergence of a restricted class of self-scaling methods with inexact line searches, for convex functions. / Al-Baali, M.
In: Computational Optimization and Applications, Vol. 9, No. 2, 02.1998, p. 191-203.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Global and superlinear convergence of a restricted class of self-scaling methods with inexact line searches, for convex functions
AU - Al-Baali, M.
PY - 1998/2
Y1 - 1998/2
N2 - This paper studies the convergence properties of algorithms belonging to the class of self-scaling (SS) quasi-Newton methods for unconstrained optimization. This class depends on two parameters, say θk and τk, for which the choice τk = 1 gives the Broyden family of unscaled methods, where θk = 1 corresponds to the well known DFP method. We propose simple conditions on these parameters that give rise to global convergence with inexact line searches, for convex objective functions. The q-superlinear convergence is achieved if further restrictions on the scaling parameter are introduced. These convergence results are an extension of the known results for the unscaled methods. Because the scaling parameter is heavily restricted, we consider a subclass of SS methods which satisfies the required conditions. Although convergence for the unscaled methods with θk≥1 is still an open question, we show that the global and superlinear convergence for SS methods is possible and present, in particular, a new SS-DFP method.
AB - This paper studies the convergence properties of algorithms belonging to the class of self-scaling (SS) quasi-Newton methods for unconstrained optimization. This class depends on two parameters, say θk and τk, for which the choice τk = 1 gives the Broyden family of unscaled methods, where θk = 1 corresponds to the well known DFP method. We propose simple conditions on these parameters that give rise to global convergence with inexact line searches, for convex objective functions. The q-superlinear convergence is achieved if further restrictions on the scaling parameter are introduced. These convergence results are an extension of the known results for the unscaled methods. Because the scaling parameter is heavily restricted, we consider a subclass of SS methods which satisfies the required conditions. Although convergence for the unscaled methods with θk≥1 is still an open question, we show that the global and superlinear convergence for SS methods is possible and present, in particular, a new SS-DFP method.
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UR - http://www.scopus.com/inward/citedby.url?scp=0031999308&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0031999308
VL - 9
SP - 191
EP - 203
JO - Computational Optimization and Applications
JF - Computational Optimization and Applications
SN - 0926-6003
IS - 2
ER -