arnoldi process
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Automatica ◽  
2021 ◽  
Vol 129 ◽  
pp. 109663
Author(s):  
Jing Chen ◽  
Biao Huang ◽  
Min Gan ◽  
C.L. Philip Chen

2020 ◽  
Vol 151 ◽  
pp. 425-447 ◽  
Author(s):  
Fatemeh Panjeh Ali Beik ◽  
Mehdi Najafi–Kalyani ◽  
Lothar Reichel

2016 ◽  
Vol 23 (6) ◽  
pp. 1007-1022 ◽  
Author(s):  
Miroslav S. Pranić ◽  
Lothar Reichel ◽  
Giuseppe Rodriguez ◽  
Zhengsheng Wang ◽  
Xuebo Yu
Keyword(s):  

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Chien Li ◽  
Jen-Yuan Chen ◽  
David R. Kincaid

We discuss a variety of iterative methods that are based on the Arnoldi process for solving large sparse symmetric indefinite linear systems. We describe the SYMMLQ and SYMMQR methods, as well as generalizations and modifications of them. Then, we cover the Lanczos/MSYMMLQ and Lanczos/MSYMMQR methods, which arise from a double linear system. We present pseudocodes for these algorithms.


2013 ◽  
Vol 17 (3) ◽  
pp. 847-852 ◽  
Author(s):  
Jieer Wu ◽  
Zheshu Ma

The inverse blackbody radiation problem is focused on determining temperature distribution of a blackbody from measured total radiated power spectrum. This problem consists of solving a first kind of Fredholm integral equation and many numerical methods have been proposed. In this paper, a regularized GMRES method is presented to solve the linear ill-posed problem caused by the discretization of such an integral equation. This method projects the orignal problem onto a lower dimensional subspaces by the Arnoldi process. Tikhonov regularization combined with GCV criterion is applied to stabilize the numerical iteration process. Three numerical examples indicate the effectiveness of the regularized GMRES method.


2011 ◽  
Vol 235 (8) ◽  
pp. 2626-2639
Author(s):  
Eric King-Wah Chu ◽  
Hung-Yuan Fan ◽  
Zhongxiao Jia ◽  
Tiexiang Li ◽  
Wen-Wei Lin

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