Guang-Xin Huang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Guang-Xin Huang has a total of 25 co-authors. Here are the top co-authors by number of shared publications:
- Lothar Reichel
Department of Mathematical Sciences, Kent State University, Kent, USA
5 shared publications Feng Yin
3 shared publications- Silvia Noschese
Dipartimento di Matematica, Sapienza Università di Roma, Roma, Italy
3 shared publications - Feng Yin
Sichuan Univ Sci & Engn, Sch Math & Stat, Zigong 643000, Peoples R China
2 shared publications - Feng Yin
Geomathematics Key Laboratory of Sichuan, College of Mathematics and Physics, Chengdu University of Technology, Chengdu 610059, China
2 shared publications Ke Guo
College of Information and Management, Key laboratory of Geomathematics of Sichuan Province, Chengdu University of Technology, Chengdu, 610059, China
2 shared publications
Latest Publications
- Open Access
- 3 January 2024
- Published by MDPI AG in Mathematics
- Vol. 12 (1) , 159
Tensor Conjugate Gradient Methods with Automatically Determination of Regularization Parameters for Ill-Posed Problems with t-Product
Ill-posed problems arise in many areas of science and engineering. Tikhonov is a usual regularization which replaces the original problem by a minimization problem with a fidelity term and a regularization term. In this paper, a tensor t-production structure preserved Conjugate-Gradient (tCG) method is presented to solve the regularization minimization problem. We provide a truncated version of regularization parameters for the tCG method and a preprocessed version of the tCG method. The discrepancy principle is used to automatically determine the regularization parameter. Several examples on image and video recover are given to show the effectiveness of the proposed methods by comparing them with some previous algorithms.
- 3 January 2024
- Published by MDPI AG in Mathematics
- Vol. 12 (1) , 159
Open AccessTensor Conjugate Gradient Methods with Automatically Determination of Regularization Parameters for Ill-Posed Problems with t-Product
Ill-posed problems arise in many areas of science and engineering. Tikhonov is a usual regularization which replaces the original problem by a minimization problem with a fidelity term and a regularization term. In this paper, a tensor t-production structure preserved Conjugate-Gradient (tCG) method is presented to solve the regularization minimization problem. We provide a truncated version of regularization parameters for the tCG method and a preprocessed version of the tCG method. The discrepancy principle is used to automatically determine the regularization parameter. Several examples on image and video recover are given to show the effectiveness of the proposed methods by comparing them with some previous algorithms.
- 27 November 2023
- Published by Elsevier in Applied Mathematics and Computation
- Vol. 466
Preconditioned BiCGSTAB and BiCRSTAB methods for solving the Sylvester tensor equation
- 27 November 2023
- Published by Elsevier in Applied Mathematics and Computation
- Vol. 466
Preconditioned BiCGSTAB and BiCRSTAB methods for solving the Sylvester tensor equation
- 8 November 2023
- Published by Springer Nature in Numerical Algorithms
- Vol. 96 (4) , 1755-1778
Tensor randomized extended Kaczmarz methods for large inconsistent tensor linear equations with t-product
- 8 November 2023
- Published by Springer Nature in Numerical Algorithms
- Vol. 96 (4) , 1755-1778
Tensor randomized extended Kaczmarz methods for large inconsistent tensor linear equations with t-product
- Open Access
- 26 October 2023
- Published by MDPI AG in Preprints.org
Tensor Conjugate-Gradient Methods With Automatically Determination of Regularization Parameters for Ill-Posed Problems With T-product
This paper presents three types of tensor Conjugate-Gradient methods for solving large-scale linear discrete ill-posed problems based on the t-product between third-order tensors. An automatical determination strategy of a suitable regularization parameter is proposed for the tensor conjugate gradient (tCG) method. A truncated version and a preprocessed verion of the tCG method are further presented. The discrepancy principle is employed to determine a suitable regularization parameter. Several numerical examples are given to show the effectiveness of the proposed tCG methods in image and video restoration.
- 26 October 2023
- Published by MDPI AG in Preprints.org
Open AccessTensor Conjugate-Gradient Methods With Automatically Determination of Regularization Parameters for Ill-Posed Problems With T-product
This paper presents three types of tensor Conjugate-Gradient methods for solving large-scale linear discrete ill-posed problems based on the t-product between third-order tensors. An automatical determination strategy of a suitable regularization parameter is proposed for the tensor conjugate gradient (tCG) method. A truncated version and a preprocessed verion of the tCG method are further presented. The discrepancy principle is employed to determine a suitable regularization parameter. Several numerical examples are given to show the effectiveness of the proposed tCG methods in image and video restoration.
- 12 July 2023
- Published by Elsevier in Journal of Computational and Applied Mathematics
- Vol. 437
An almost-maximal residual tensor block Kaczmarz method for large tensor linear systems
- 12 July 2023
- Published by Elsevier in Journal of Computational and Applied Mathematics
- Vol. 437
An almost-maximal residual tensor block Kaczmarz method for large tensor linear systems
- Open Access
- 1 January 2023
- Published by American Institute of Mathematical Sciences (AIMS) in AIMS Mathematics
- Vol. 8 (11) , 26782-26800
Tensor Conjugate-Gradient methods for tensor linear discrete ill-posed problems
This paper presents three types of tensor Conjugate-Gradient (tCG) methods for solving large-scale linear discrete ill-posed problems based on the t-product between third-order tensors. An automatic determination strategy of a suitable regularization parameter is proposed for the tCG method in the Fourier domain (A-tCG-FFT). An improved version and a preconditioned version of the tCG method are also presented. The discrepancy principle is employed to determine a suitable regularization parameter. Several numerical examples in image and video restoration are given to show the effectiveness of the proposed tCG methods.
- 1 January 2023
- Published by American Institute of Mathematical Sciences (AIMS) in AIMS Mathematics
- Vol. 8 (11) , 26782-26800
Open AccessTensor Conjugate-Gradient methods for tensor linear discrete ill-posed problems
This paper presents three types of tensor Conjugate-Gradient (tCG) methods for solving large-scale linear discrete ill-posed problems based on the t-product between third-order tensors. An automatic determination strategy of a suitable regularization parameter is proposed for the tCG method in the Fourier domain (A-tCG-FFT). An improved version and a preconditioned version of the tCG method are also presented. The discrepancy principle is employed to determine a suitable regularization parameter. Several numerical examples in image and video restoration are given to show the effectiveness of the proposed tCG methods.
- Open Access
- 19 July 2022
- Published by Springer Nature in Research Square
A partially block randomized extended Kaczmarzmethod for solving large overdetermined insistentlinear systems
This paper presents a partial block random extended Kaczmarz (PBREK) method for solving large overdetermined inconsistent linear system of equations $Ax=b$. It is proved that the proposed PBREK method converges to the least squares solution of $Ax=b$. Several examples are given to show the effectiveness of the proposed PBREK method, and it is compared with the PREK algorithm.- 19 July 2022
- Published by Springer Nature in Research Square
Open AccessA partially block randomized extended Kaczmarzmethod for solving large overdetermined insistentlinear systems
This paper presents a partial block random extended Kaczmarz (PBREK) method for solving large overdetermined inconsistent linear system of equations $Ax=b$. It is proved that the proposed PBREK method converges to the least squares solution of $Ax=b$. Several examples are given to show the effectiveness of the proposed PBREK method, and it is compared with the PREK algorithm. - Open Access
- 1 January 2022
- Published by American Institute of Mathematical Sciences (AIMS) in Electronic Research Archive
- Vol. 30 (6) , 2196-2204
New error bounds for the tensor complementarity problem
This paper discusses new error bounds for the tensor complementarity problem using a $ P $-tensor. A new lower error bound and a global error bound are presented for such a problem. It is proved that the norm of the exact solution of the tensor complementarity problem with a $ P $-tensor has a lower bound and an upper bound. When the order of a tensor is $ 2 $, all the results for the tensor complementarity problem obtained reduce to those for the linear complementarity problem.- 1 January 2022
- Published by American Institute of Mathematical Sciences (AIMS) in Electronic Research Archive
- Vol. 30 (6) , 2196-2204
Open AccessNew error bounds for the tensor complementarity problem
This paper discusses new error bounds for the tensor complementarity problem using a $ P $-tensor. A new lower error bound and a global error bound are presented for such a problem. It is proved that the norm of the exact solution of the tensor complementarity problem with a $ P $-tensor has a lower bound and an upper bound. When the order of a tensor is $ 2 $, all the results for the tensor complementarity problem obtained reduce to those for the linear complementarity problem. - 1 June 2021
- Published by Elsevier in Applied Numerical Mathematics
- Vol. 164, 211-221
On the choice of regularization matrix for an ℓ2-ℓ minimization method for image restoration
- 1 June 2021
- Published by Elsevier in Applied Numerical Mathematics
- Vol. 164, 211-221
On the choice of regularization matrix for an ℓ2-ℓ minimization method for image restoration