Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/18354
Title: | Descent direction method with line search for unconstrained optimization in noisy environment | Authors: | Krejić Nataša Luzanin Zorana Ovcin Zoran Stojkovska I. |
Issue Date: | 2015 | Journal: | Optimization Methods and Software | Abstract: | © 2015 Taylor & Francis. A two-phase descent direction method for unconstrained stochastic optimization problem is proposed. A line-search method with an arbitrary descent direction is used to determine the step sizes during the initial phase, and the second phase performs the stochastic approximation (SA) step sizes. The almost sure convergence of the proposed method is established, under standard assumption for descent direction and SA methods. The algorithm used for practical implementation combines a line-search quasi-Newton (QN) method, in particular the Broyden-Fletcher-Goldfarb-Shanno (BFGS) and Symmetric Rank 1 (SR1) methods, with the SA iterations. Numerical results show good performance of the proposed method for different noise levels. | URI: | https://open.uns.ac.rs/handle/123456789/18354 | ISSN: | 1055-6788 | DOI: | 10.1080/10556788.2015.1025403 |
Appears in Collections: | PMF Publikacije/Publications |
Show full item record
SCOPUSTM
Citations
10
checked on Aug 12, 2023
Page view(s)
28
Last Week
7
7
Last month
0
0
checked on May 10, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.