Leong, Wah June and Enshaei, Sharareh and Kek, Sie Long (2021) Diagonal quasi-Newton methods via least change updating principle with weighted Frobenius norm. Numerical Algorithms, 86. pp. 1225-1241.
![[img]](http://eprints.uthm.edu.my/style/images/fileicons/text.png) |
Text
J12010_5a7e0282f614f0890c3fed53dbfc3ddd.pdf Restricted to Registered users only Download (451kB) | Request a copy |
Abstract
This paper presents a class of low memory quasi-Newton methods with standard backtracking line search for large-scale unconstrained minimization. The methods are derived by means of least change updating technique analogous to that for the DFP method except that the full quasi-Newton matrix has been replaced by some diagonal matrix. We establish convergence properties for some particular members of the class under line search with Armijo condition. Sufficient conditions for the methods to be superlinearly convergent are also given. Numerical results are then presented to illustrate the usefulness of these methods in large-scale minimization.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Nonlinear minimization; Quasi-Newton methods; diagonal updating; least change updating technique; weighted frobenius norm. |
| Subjects: | T Technology > T Technology (General) |
| Depositing User: | Mr. Abdul Rahim Mat Radzuan |
| Date Deposited: | 17 Oct 2021 07:48 |
| Last Modified: | 17 Oct 2021 07:48 |
| URI: | http://eprints.uthm.edu.my/id/eprint/1127 |
Actions (login required)
 |
View Item |