- Journal Home
- Volume 41 - 2025
- Volume 40 - 2024
- Volume 39 - 2023
- Volume 38 - 2022
- Volume 37 - 2021
- Volume 36 - 2020
- Volume 35 - 2019
- Volume 34 - 2018
- Volume 33 - 2017
- Volume 32 - 2016
- Volume 31 - 2015
- Volume 30 - 2014
- Volume 29 - 2013
- Volume 28 - 2012
- Volume 27 - 2011
- Volume 26 - 2010
- Volume 25 - 2009
Cited by
- BibTex
- RIS
- TXT
Recently, Yu et al. presented a modified fixed point iterative (MFPI) method for solving large sparse absolute value equation (AVE). In this paper, we consider using accelerated overrelaxation (AOR) splitting to develop the modified fixed point iteration (denoted by MFPI-JS and MFPI-GSS) methods for solving AVE. Furthermore, the convergence analysis of the MFPI-JS and MFPI-GSS methods for AVE are also studied under suitable restrictions on the iteration parameters, and the functional equation between the parameter $\tau$ and matrix $Q.$ Finally, numerical examples show that the MFPI-JS and MFPI-GSS are efficient iteration methods.
}, issn = {2707-8523}, doi = {https://doi.org/10.4208/cmr.2024-0009}, url = {http://global-sci.org/intro/article_detail/cmr/23412.html} }Recently, Yu et al. presented a modified fixed point iterative (MFPI) method for solving large sparse absolute value equation (AVE). In this paper, we consider using accelerated overrelaxation (AOR) splitting to develop the modified fixed point iteration (denoted by MFPI-JS and MFPI-GSS) methods for solving AVE. Furthermore, the convergence analysis of the MFPI-JS and MFPI-GSS methods for AVE are also studied under suitable restrictions on the iteration parameters, and the functional equation between the parameter $\tau$ and matrix $Q.$ Finally, numerical examples show that the MFPI-JS and MFPI-GSS are efficient iteration methods.