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张量是数值多重线性代数的主要研究对象,其在量子力学、心理测量学、化学计量学、信号处理、高阶统计等领域有重要应用[1-3]. 张量是向量和矩阵的高阶推广,它的许多性质与矩阵情形类似,但也有很大不同[4]. 张量相关问题的研究要比矩阵情形复杂得多. 目前,在张量分解、张量的低秩逼近、张量互补问题、张量特征值问题和张量方程等方面已有诸多研究成果[4-9]. 本文考虑基于Einstein积[10]的一类张量方程的求解问题.
为了表述方便,首先介绍本文用到的一些记号和定义. m-阶I1×I2×…×Im-维复张量A=(ai1i2… im),ai1i2… im∈
$ \mathbb{C} $ ,1≤ij≤Ij(j=1,2,…,m). 我们用$ \mathbb{C} $ I1×I2×…×Im表示所有m-阶I1×I2×…×Im-维复张量的全体. 本文中In=(ei1i2… inj1j2… jn)∈$ \mathbb{R} $ I1×I2×…×In×I1×I2×…×In表示单位张量,它的元素定义为ei1i2… inj1j2… jn=$ \mathop \Pi \limits_{k = 1}^n $ δikjk,这里δikjk=1如果ik=jk,否则δikjk=0.若张量A=(ai1i2… imj1j2… jn)∈
$ \mathbb{C} $ I1×I2×…×Im×J1×J2×…×Jn,B=(bj1j2… jnk1k2… kp)∈$ \mathbb{C} $ J1×J2×…×Jn×K1×K2×…×Kp,则它们的Einstein积A*nB为I1×I2×…×Im×K1×K2×…×Kp-维张量,依据文献[7]定义其元素为进一步,设张量S=(ai1i2… imj1j2… jn),T=(bk1k2… kml1l2… ln)且S,T∈
$ \mathbb{C} $ I1×I2×…×Im×J1×J2×…×Jn,则其内积定义为张量的内积诱导出张量的Frobenius范数,即对于张量T有‖T‖=
$\sqrt {\left\langle {\mathit{\boldsymbol{T}}, \mathit{\boldsymbol{T}}} \right\rangle } $ .定义1 设张量A=(ai1i2… imj1j2… jn)∈
$ \mathbb{C} $ I1×I2×…×Im×J1×J2×…×Jn,则其共轭转置AH定义为若张量A∈
$ \mathbb{C} $ I1×I2×…×Im×I1×I2×…×Im满足条件AH=A,则称之为Hermitian张量.若定义1中A是实张量,则共轭转置退化为转置[7]. 值得一提的是,最新的研究发现,Hermitian张量在量子纠缠中有实际应用[11]. 本文考虑张量方程
的Hermitian解,这里A,B∈
$ \mathbb{C} $ P1×P2×…×Pm×I1×I2×…×In为已知张量,X∈$ \mathbb{C} $ I1×I2×…×In×I1×I2×…×In为未知的Hermitian张量. 张量方程(1)在控制系统、连续力学等领域有实际应用[7, 11]. 例如,对于2-维泊松方程Ω={(x,y)|0≤x,y≤1},利用中心差分格式,可以离散为张量方程[7]
这里张量A∈
$ \mathbb{R} $ N×N×N×N的非零元为F∈
$ \mathbb{R} $ N×N,对于没有约束条件的张量方程(1),文献[7]引入了张量逆的概念,得到了它的最小二乘解. 进一步,作为张量逆的推广形式,文献[12]提出了张量的Moore-Penrose广义逆,并讨论了张量方程(1)的可解性及其通解形式.在图像处理等领域的应用中,考虑方程解的特殊结构是降低算法复杂度的重要途径[13],但是对于带有约束条件的形如方程(1)的张量方程求解问题尚无研究. 借助张量的Moore-Penrose广义逆的性质,我们将建立张量方程组(1)有Hermitian解的充要条件,并得到有解时的一般解表达式. 进一步,将考虑张量方程(1)约束下的张量逼近问题.
设X0∈
$ \mathbb{C} $ I1×I2×…×In×I1×I2×…×In为一给定张量,求Hermitian张量$ {\mathop {\boldsymbol{X}}\limits^ \wedge } $ ∈$ \mathbb{C} $ I1×I2×…×In×I1×I2×…×In使之满足等式其中Θ表示张量方程(1)的所有Hermitian解的集合.
矩阵最佳逼近问题在有限元、控制论等领域具有实际应用,且已被深入研究[14-16]. 张量最佳逼近问题(3)可以看作是矩阵情形的直接推广形式,同时也可以看作是张量完全问题、张量低秩逼近问题等应用型问题的一般形式[17-19]. 在式(3)中,张量X0由实际测量或试验观测所得,但由于误差原因,它往往不满足所要求的特殊结构或性质,而
$ {\mathop {\boldsymbol{X}}\limits^ \wedge } $ 是满足实际需求的张量. 若解集合Θ非空,可以证明上述张量逼近问题的解是唯一的,且可以利用已知张量的Moore-Penrose逆具体表示.
Solvability Conditions for a Class of Tensor Equations and Associated Optimal Approximation Problems
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摘要: 研究了张量方程A*nX=B具有Hermitian解X的可解性问题,其中*n表示张量的Einstein积. 利用张量Moore-Penrose广义逆的性质,得到了该方程具有Hermitian解的充要条件及其通解表达式. 同时,在张量的Frobenius范数意义下,考虑了对于任意给定张量的最佳逼近问题,得到了它的唯一解表达式. 最后,通过数值例子说明了结论的可行性.
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关键词:
- 张量方程 /
- Hermitian张量 /
- Moore-Penrose广义逆 /
- 最佳逼近
Abstract: This paper is concerned with the solution to the tensor equation A*nX=B with Hermitian X, where represents the Einstein product. Depending on the properties of Moore-Penrose generalized inverses of tensors, the solvability conditions for the existence of the Hermitian solution to the above tensor equation as well as its general solution have been derived. Meanwhile, the associated tensor approximation problem for any given tensor has been considered and the unique solution has been given. Finally, the performed numerical results demonstrate the feasibility of the proposed results.-
Key words:
- tensor equations /
- Hermitian tensors /
- Moore-Penrose generalized inverses /
- optimal approximation .
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[1] COPPI R, BOLASCO S. Multiway Data Analysis[M]. Amsterdam: Elsevier, 1989. [2] QI L Q, LUO Z Y. Tensor Analysis[M]. Philadelphia: Society for Industrial and Applied Mathematics, 2017. [3] LAI W, RUBIN D, KREMPL E. Introduction To Continuum Mechanics [M]. Amsterdam: Elsevier, 2009. [4] KOLDA T G, BADER B W. Tensor Decompositions and Applications [J]. SIAM Review, 2009, 51(3): 455-500. doi: 10.1137/07070111X [5] DE SILVA V, LIM L H. Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem [J]. SIAM Journal on Matrix Analysis and Applications, 2008, 30(3): 1084-1127. doi: 10.1137/06066518X [6] QI L Q. Eigenvalues of a Real Supersymmetric Tensor [J]. Journal of Symbolic Computation, 2005, 40(6): 1302-1324. doi: 10.1016/j.jsc.2005.05.007 [7] BRAZELL M, LI N, NAVASCA C, et al. Solving Multilinear Systems via Tensor Inversion [J]. SIAM Journal on Matrix Analysis and Applications, 2013, 34(2): 542-570. doi: 10.1137/100804577 [8] LI X T, NG M K. Solving Sparse Non-Negative Tensor Equations: Algorithms and Applications [J]. Frontiers of Mathematics in China, 2015, 10(3): 649-680. doi: 10.1007/s11464-014-0377-3 [9] DING W Y, WEI Y M. Solving Multi-Linear Systems with M-Tensors [J]. Journal of Scientific Computing, 2016, 68(2): 689-715. doi: 10.1007/s10915-015-0156-7 [10] EINSTEIN A. The Foundation of the General Theory of Relativity [M]// KOX A, KLEIN M, SCHULMANN R. The Collected Papers of Albert Einstein. Princeton: Princeton University Press, 2007: 146-200. [11] NI G. Hermitian Tensor And Quantum Mixed State[EB/OL]. (2019-08-23)[2021-04-01]. https://arxiv.org/pdf/1902.02640.pdf. [12] SUN L Z, ZHENG B D, BU C J, et al. Moore-Penrose Inverse of Tensors via Einstein Product [J]. Linear and Multilinear Algebra, 2016, 64(4): 686-698. doi: 10.1080/03081087.2015.1083933 [13] PENG Y X, HU X Y, ZHANG L. An Iteration Method for the Symmetric Solutions and the Optimal Approximation Solution of the Matrix Equation AXB=C [J]. Applied Mathematics and Computation, 2005, 160(3): 763-777. doi: 10.1016/j.amc.2003.11.030 [14] HIGHAM N J. Computing a Nearest Symmetric Positive Semidefinite Matrix [J]. Linear Algebra and Its Applications, 1988, 103: 103-118. doi: 10.1016/0024-3795(88)90223-6 [15] YUAN Y X, DAI H. The Nearness Problems for Symmetric Matrix with a Submatrix Constraint [J]. Journal of Computational and Applied Mathematics, 2008, 213(1): 224-231. doi: 10.1016/j.cam.2007.01.033 [16] HUANG G X, NOSCHESE S, REICHEL L. Regularization Matrices Determined by Matrix Nearness Problems [J]. Linear Algebra and Its Applications, 2016, 502: 41-57. doi: 10.1016/j.laa.2015.12.008 [17] ZHANG T, GOLUB G H. Rank-One Approximation to High Order Tensors [J]. SIAM Journal on Matrix Analysis and Applications, 2001, 23(2): 534-550. doi: 10.1137/S0895479899352045 [18] GANDY S, RECHT B, YAMADA I. Tensor Completion and Low-n-Rank Tensor Recovery via Convex Optimization [J]. Inverse Problems, 2011, 27(2): 025010. doi: 10.1088/0266-5611/27/2/025010 [19] doi: http://www.researchgate.net/profile/Bing_Zheng3/publication/329275955_Further_results_on_Moore-Penrose_inverses_of_tensors_with_application_to_tensor_nearness_problems/links/5c05e1c5a6fdcc315f9ae237/Further-results-on-Moore-Penrose-inverses-of-tensors-with-application-to-tensor-nearness-problems.pdf LIANG M L, ZHENG B. Further Results on Moore-Penrose Inverses of Tensors with Application to Tensor Nearness Problems [J]. Computers & Mathematics With Applications, 2019, 77(5): 1282-1293. [20] BADER B, KOLDA T, MAYO J, et al. MATLAB Tensor Toolbox Version 3.2[EB/OL]. (2015-02-18)[2020-12-15]. http://www.tensortoolbox.org.