Hai-Hua Qin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Hai-Hua Qin has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Hong-Kui Pang
School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou, People’s Republic of China
3 shared publications - Hai-Wei Sun
Department of Mathematics, University of Macau, Macao, China
2 shared publications Hai‐Wei Sun
Department of Mathematics University of Macau Macau People's Republic of China
1 shared publicationHong‐Kui Pang
School of Mathematics and Statistics & Research Institute of Mathematical Science Jiangsu Normal University Xuzhou Jiangsu People's Republic of China
1 shared publication- Shuai Ni
School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou, People’s Republic of China
1 shared publication - Ting-Ting Ma
School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou, 221116, Jiangsu, PR China
1 shared publication
Latest Publications
- 25 July 2024
- Published by Springer Nature in Journal of Scientific Computing
- Vol. 100 (3) , 1-32
Sine Transform Based Preconditioning for an Inverse Source Problem of Time-Space Fractional Diffusion Equations
- 25 July 2024
- Published by Springer Nature in Journal of Scientific Computing
- Vol. 100 (3) , 1-32
Sine Transform Based Preconditioning for an Inverse Source Problem of Time-Space Fractional Diffusion Equations
- 7 October 2022
- Published by Wiley in Numerical Linear Algebra with Applications
- Vol. 30 (4)
Sine transform based preconditioning techniques for space fractional diffusion equations
We study the preconditioned iterative methods for the linear systems arising from the numerical solution of the multi‐dimensional space fractional diffusion equations. A sine transform based preconditioning technique is developed according to the symmetric and skew‐symmetric splitting of the Toeplitz factor in the resulting coefficient matrix. Theoretical analyses show that the upper bound of relative residual norm of the GMRES method when applied to the preconditioned linear system is mesh‐independent which implies the linear convergence. Numerical experiments are carried out to illustrate the correctness of the theoretical results and the effectiveness of the proposed preconditioning technique.
- 7 October 2022
- Published by Wiley in Numerical Linear Algebra with Applications
- Vol. 30 (4)
Sine transform based preconditioning techniques for space fractional diffusion equations
We study the preconditioned iterative methods for the linear systems arising from the numerical solution of the multi‐dimensional space fractional diffusion equations. A sine transform based preconditioning technique is developed according to the symmetric and skew‐symmetric splitting of the Toeplitz factor in the resulting coefficient matrix. Theoretical analyses show that the upper bound of relative residual norm of the GMRES method when applied to the preconditioned linear system is mesh‐independent which implies the linear convergence. Numerical experiments are carried out to illustrate the correctness of the theoretical results and the effectiveness of the proposed preconditioning technique.
- 14 August 2021
- Published by Springer Nature in Numerical Algorithms
- Vol. 90 (1) , 31-57
All-at-once method for variable-order time fractional diffusion equations
- 14 August 2021
- Published by Springer Nature in Numerical Algorithms
- Vol. 90 (1) , 31-57
All-at-once method for variable-order time fractional diffusion equations
- 26 January 2021
- Published by Elsevier in Computers & Mathematics with Applications
- Vol. 85, 18-29
Circulant-based approximate inverse preconditioners for a class of fractional diffusion equations
- 26 January 2021
- Published by Elsevier in Computers & Mathematics with Applications
- Vol. 85, 18-29
Circulant-based approximate inverse preconditioners for a class of fractional diffusion equations
- 1 July 2016
- Published by Elsevier in Applied Numerical Mathematics
- Vol. 105, 82-95
The linear sampling method for inhomogeneous medium and buried objects from far field measurements
- 1 July 2016
- Published by Elsevier in Applied Numerical Mathematics
- Vol. 105, 82-95
The linear sampling method for inhomogeneous medium and buried objects from far field measurements