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Least Squares Hermitian Problem of Matrix Equation (<i>AXB</i>, <i>CXD</i>) = (<i>E</i>, <i>F</i>) Associated with Indeterminate Admittance Matrices

Least Squares Hermitian Problem of Matrix Equation (<i>AXB</i>, <i>CXD</i>) = (<i>E</i>, <i>F</i>) Associated with Indeterminate Admittance Matrices
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摘要 For A∈CmΧn, if the sum of the elements in each row and the sum of the elements in each column are both equal to 0, then A is called an indeterminate admittance matrix. If A is an indeterminate admittance matrix and a Hermitian matrix, then A is called a Hermitian indeterminate admittance matrix. In this paper, we provide two methods to study the least squares Hermitian indeterminate admittance problem of complex matrix equation (AXB,CXD)=(E,F), and give the explicit expressions of least squares Hermitian indeterminate admittance solution with the least norm in each method. We mainly adopt the Moore-Penrose generalized inverse and Kronecker product in Method I and a matrix-vector product in Method II, respectively. For A∈CmΧn, if the sum of the elements in each row and the sum of the elements in each column are both equal to 0, then A is called an indeterminate admittance matrix. If A is an indeterminate admittance matrix and a Hermitian matrix, then A is called a Hermitian indeterminate admittance matrix. In this paper, we provide two methods to study the least squares Hermitian indeterminate admittance problem of complex matrix equation (AXB,CXD)=(E,F), and give the explicit expressions of least squares Hermitian indeterminate admittance solution with the least norm in each method. We mainly adopt the Moore-Penrose generalized inverse and Kronecker product in Method I and a matrix-vector product in Method II, respectively.
出处 《Journal of Applied Mathematics and Physics》 2018年第6期1199-1214,共16页 应用数学与应用物理(英文)
关键词 Matrix Equation Least Squares Solution Least Norm Solution HERMITIAN INDETERMINATE ADMITTANCE MATRICES Matrix Equation Least Squares Solution Least Norm Solution Hermitian Indeterminate Admittance Matrices
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