Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Qing-Biao Wu"'
Publikováno v:
Complexity, Vol 2021 (2021)
This paper proposes the modified generalization of the HSS (MGHSS) to solve a large and sparse continuous Sylvester equation, improving the efficiency and robustness. The analysis shows that the MGHSS converges to the unique solution of AX + XB = C u
Externí odkaz:
https://doaj.org/article/a219ad08b0b54a28b1e08363d31f1669
Autor:
Yu-Ye Feng, Qing-Biao Wu
Publikováno v:
Journal of Mathematics, Vol 2021 (2021)
For solving the large sparse linear systems with 2×2 block structure, the generalized successive overrelaxation (GSOR) iteration method is an efficient iteration method. Based on the GSOR method, the PGSOR method introduces a preconditioned matrix w
Externí odkaz:
https://doaj.org/article/fe99e8a5be964d949edbefdaffcacfff
Publikováno v:
Complexity, Vol 2021 (2021)
This paper proposes the modified generalization of the HSS (MGHSS) to solve a large and sparse continuous Sylvester equation, improving the efficiency and robustness. The analysis shows that the MGHSS converges to the unique solution of AX + XB = C u
Publikováno v:
Computational and Applied Mathematics. 40
In this study, based on the double-step scale splitting (DSS) iteration method for solving complex Sylvester matrix equation, we propose two corresponding lopsided DSS iteration methods. These new methods, LDSS1 and LDSS2, are proved to be convergent
Publikováno v:
IET Renewable Power Generation. 13:2062-2069
Wind speed forecasting is important for high-efficiency utilisation of wind energy and management of grid-connected power systems. Due to the noise, instability and irregularity of atmosphere system, the current models based on raw historical data ha
Publikováno v:
Physica A: Statistical Mechanics and its Applications. 516:114-124
Considering the actual demand of crude oil price forecasting, a novel model based on ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) is proposed. In practical work, the model trained by historical data will be used in l
Autor:
Qing-Biao Wu, Yuye Feng
This paper proposes the modified generalization of the HSS (MGHSS) to solve a large and sparse CS equation, improving the efficiency and robustness. The analysis shows that the MGHSS converges to the unique solution of AX+XB=C unconditionally. We als
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d69fd6f4f388d85fb7440f5cd991d346
https://doi.org/10.20944/preprints202012.0022.v1
https://doi.org/10.20944/preprints202012.0022.v1
Autor:
Yuye Feng, Qing-Biao Wu
This paper is concerned with the modification of a generalization of the Hermitian and skew-Hermitian splitting iteration method for solving large sparse continuous Sylvester equations. The analysis shows that the MGHSS iteration method converges unc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3b478d731012e0524ec5d36179e85b28
https://doi.org/10.20944/preprints202011.0444.v1
https://doi.org/10.20944/preprints202011.0444.v1
Publikováno v:
Computational and Applied Mathematics. 38
It has been proved that Newton–HSS method is efficient and robust for solving large sparse systems of nonlinear equations with positive definite Jacobian matrices at the solution points. In this paper, by utilizing the single-step Hermitian and ske
Autor:
Jie Bao, Yong-Hao Chen, Xian-Peng Zhang, Guang-Yuan Luan, Jie Ren, Qi Wang, Xi-Chao Ruan, Kai Zhang, Qi An, Huai-Yong Bai, Ping Cao, Qi-Ping Chen, Pin-Jing Cheng, Zeng-Qi Cui, Rui-Rui Fan, Chang-Qing Feng, Min-Hao Gu, Feng-Qin Guo, Chang-Cai Han, Zi-Jie Han, Guo-Zhu He, Yong-Cheng He, Yue-Feng He, Han-Xiong Huang, Wei-Ling Huang, Xi-Ru Huang, Xiao-Lu Ji, Xu-Yang Ji, Hao-Yu Jiang, Wei Jiang, Han-Tao Jing, Ling Kang, Ming-Tao Kang, Chang-Lin Lan, Bo Li, Lun Li, Qiang Li, Xiao Li, Yang Li, Rong Liu, Shu-Bin Liu, Xing-Yan Liu, Ying-Lin Ma, Chang-Jun Ning, Yang-Bo Nie, Bin-Bin Qi, Zhao-Hui Song, Hong Sun, Xiao-Yang Sun, Zhi-Jia Sun, Zhi-Xin Tan, Hong-Qing Tang, Jing-Yu Tang, Peng-Cheng Wang, Tao-Feng Wang, Yan-Feng Wang, Zhao-Hui Wang, Zheng Wang, Jie Wen, Zhong-Wei Wen, Qing-Biao Wu, Xiao-Guang Wu, Xuan Wu, Li-Kun Xie, Yi-Wei Yang, Yi Yang, Han Yi, Li Yu, Tao Yu, Yong-Ji Yu, Guo-Hui Zhang, Jing Zhang, Lin-Hao Zhang, Li-Ying Zhang, Qing-Min Zhang, Qi-Wei Zhang, Yu-Liang Zhang, Zhi-Yong Zhang, Ying-Tan Zhao, Liang Zhou, Zu-Ying Zhou, Dan-Yang Zhu, Ke-Jun Zhu, Peng Zhu
Publikováno v:
Acta Physica Sinica. 68:109901