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2020 – today
- 2025
[j35]Maolin Che, Yimin Wei, Hong Yan:
Efficient Randomized Algorithms for Fixed Precision Problem of Approximate Tucker Decomposition. SIAM J. Matrix Anal. Appl. 46(1): 256-297 (2025)- [i4]Yuchao Wang, Maolin Che, Yimin Wei:
Effective algorithms for tensor train decomposition via the UTV framework. CoRR abs/2501.07904 (2025) - 2024
- [j34]Yonghe Liu, Fengsheng Wu
, Maolin Che, Chaoqian Li:
Fixed-precision randomized quaternion singular value decomposition algorithm for low-rank quaternion matrix approximations. Neurocomputing 580: 127490 (2024) - [j33]Maolin Che, Yimin Wei, Hong Yan:
Sketch-based multiplicative updating algorithms for symmetric nonnegative tensor factorizations with applications to face image clustering. J. Glob. Optim. 89(4): 995-1032 (2024) - [j32]Hao Zhang, Ting-Zhu Huang, Xi-Le Zhao, Maolin Che:
A Fast Algorithm for Rank-(L, M, N) Block Term Decomposition of Multi-Dimensional Data. J. Sci. Comput. 101(1): 16 (2024) - [j31]Chaoqian Li, Yonghe Liu, Fengsheng Wu, Maolin Che:
Randomized block Krylov subspace algorithms for low-rank quaternion matrix approximations. Numer. Algorithms 96(2): 687-717 (2024) - [i3]Maolin Che, Yimin Wei, Hong Yan:
Randomized algorithms for computing the tensor train approximation and their applications. CoRR abs/2405.07147 (2024) - 2023
- [j30]Xuezhong Wang, Maolin Che, Changxin Mo, Yimin Wei:
Solving the system of nonsingular tensor equations via randomized Kaczmarz-like method. J. Comput. Appl. Math. 421: 114856 (2023) - [j29]Maolin Che, Yimin Wei, Yanwei Xu:
Randomized algorithms for the computation of multilinear rank-(μ 1,μ 2,μ 3) approximations. J. Glob. Optim. 87(2): 373-403 (2023) - [i2]Maolin Che, Yimin Wei, Hong Yan:
Efficient algorithms for Tucker decomposition via approximate matrix multiplication. CoRR abs/2303.11612 (2023) - 2022
- [j28]Xuezhong Wang, Maolin Che, Yimin Wei:
Randomized Kaczmarz methods for tensor complementarity problems. Comput. Optim. Appl. 82(3): 595-615 (2022) - [j27]Maolin Che, Juefei Chen, Yimin Wei:
Perturbations of the Tcur Decomposition for Tensor Valued Data in the Tucker Format. J. Optim. Theory Appl. 194(3): 852-877 (2022) - [j26]Maolin Che, Yimin Wei:
An Efficient Algorithm for Computing the Approximate t-URV and its Applications. J. Sci. Comput. 92(3): 93 (2022) - [j25]Maolin Che, Xuezhong Wang, Yimin Wei, Xile Zhao:
Fast randomized tensor singular value thresholding for low-rank tensor optimization. Numer. Linear Algebra Appl. 29(6) (2022) - 2021
- [j24]Maolin Che, Yimin Wei, Hong Yan:
Randomized algorithms for the low multilinear rank approximations of tensors. J. Comput. Appl. Math. 390: 113380 (2021) - [j23]Maolin Che, Yimin Wei, Hong Yan:
An Efficient Randomized Algorithm for Computing the Approximate Tucker Decomposition. J. Sci. Comput. 88(1): 32 (2021) - [j22]Xuezhong Wang, Changxin Mo, Maolin Che, Yimin Wei:
Accelerated dynamical approaches for finding the unique positive solution of KS-tensor equations. Numer. Algorithms 88(4): 1787-1810 (2021) - 2020
- [j21]Shouqiang Du, Maolin Che, Yimin Wei:
Stochastic structured tensors to stochastic complementarity problems. Comput. Optim. Appl. 75(3): 649-668 (2020) - [j20]Xuezhong Wang, Maolin Che, Yimin Wei:
Tensor neural network models for tensor singular value decompositions. Comput. Optim. Appl. 75(3): 753-777 (2020) - [j19]Xuezhong Wang, Maolin Che, Yimin Wei:
Neural network approach for solving nonsingular multi-linear tensor systems. J. Comput. Appl. Math. 368 (2020) - [j18]Maolin Che, Yimin Wei:
Multiplicative Algorithms for Symmetric Nonnegative Tensor Factorizations and Its Applications. J. Sci. Comput. 83(3): 53 (2020) - [j17]Xuezhong Wang, Maolin Che, Yimin Wei:
Global uniqueness and solvability of tensor complementarity problems for ℋ+-tensors. Numer. Algorithms 84(2): 567-590 (2020) - [j16]Xuezhong Wang, Maolin Che, Yimin Wei:
Preconditioned tensor splitting AOR iterative methods for ℋℋ ℋ -tensor equations. Numer. Linear Algebra Appl. 27(6) (2020) - [j15]Maolin Che, Xuezhong Wang, Yimin Wei:
A Unified Self-Stabilizing Neural Network Algorithm for Principal Takagi Component Extraction. Neural Process. Lett. 51(1): 591-610 (2020) - [j14]Xuezhong Wang, Maolin Che, Liqun Qi, Yimin Wei:
Modified gradient dynamic approach to the tensor complementarity problem. Optim. Methods Softw. 35(2): 394-415 (2020) - [j13]Maolin Che, Yimin Wei, Hong Yan:
The Computation of Low Multilinear Rank Approximations of Tensors via Power Scheme and Random Projection. SIAM J. Matrix Anal. Appl. 41(2): 605-636 (2020)
2010 – 2019
- 2019
- [j12]Maolin Che, Yimin Wei:
Randomized algorithms for the approximations of Tucker and the tensor train decompositions. Adv. Comput. Math. 45(1): 395-428 (2019) - [j11]Xuezhong Wang, Maolin Che, Yimin Wei:
Existence and uniqueness of positive solution for H+-tensor equations. Appl. Math. Lett. 98: 191-198 (2019) - [j10]Xuezhong Wang, Maolin Che, Yimin Wei:
Neural networks based approach solving multi-linear systems with M-tensors. Neurocomputing 351: 33-42 (2019) - [j9]Maolin Che, Liqun Qi, Yimin Wei:
Stochastic $$R_0$$ R 0 tensors to stochastic tensor complementarity problems. Optim. Lett. 13(2): 261-279 (2019) - [i1]Maolin Che, Yimin Wei, Hong Yan:
Randomized algorithms for the low multilinear rank approximations of tensors. CoRR abs/1908.11031 (2019) - 2018
- [j8]Maolin Che, Liqun Qi, Yimin Wei, Guofeng Zhang:
Geometric measures of entanglement in multipartite pure states via complex-valued neural networks. Neurocomputing 313: 25-38 (2018) - [j7]Maolin Che, Sanzheng Qiao, Yimin Wei:
Adaptive algorithms for computing the principal Takagi vector of a complex symmetric matrix. Neurocomputing 317: 79-87 (2018) - [j6]Xuezhong Wang, Maolin Che, Yimin Wei:
Partial orthogonal rank-one decomposition of complex symmetric tensors based on the Takagi factorization. J. Comput. Appl. Math. 332: 56-71 (2018) - 2017
- [j5]Xuezhong Wang, Maolin Che, Yimin Wei:
Complex-valued neural networks for the Takagi vector of complex symmetric matrices. Neurocomputing 223: 77-85 (2017) - [j4]Maolin Che, Andrzej Cichocki, Yimin Wei:
Neural networks for computing best rank-one approximations of tensors and its applications. Neurocomputing 267: 114-133 (2017) - [j3]Maolin Che, Liqun Qi, Yimin Wei:
Iterative algorithms for computing US- and U-eigenpairs of complex tensors. J. Comput. Appl. Math. 317: 547-564 (2017) - 2016
- [j2]Xuezhong Wang, Maolin Che, Yimin Wei:
Recurrent neural network for computation of generalized eigenvalue problem with real diagonalizable matrix pair and its applications. Neurocomputing 216: 230-241 (2016) - [j1]Maolin Che, Liqun Qi, Yimin Wei:
Positive-Definite Tensors to Nonlinear Complementarity Problems. J. Optim. Theory Appl. 168(2): 475-487 (2016)
Coauthor Index
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