One kind of inverse eigenvalue problems, whose solutions are required to be normal or diagonalizable matrices, is investigated in quaternionic quantum mechanics.

摘要本文研究了四元数量子力学中类要求其解是正规或可四元数矩阵的征值反问题。

This paper discusses the structure, calculation of multiplication and power, eigenvalue and eigenvector, and diagonalizable problems of matrix of rank equal to 1.

摘要秩等于1的矩阵的结构、乘法与乘幂运、征值与征向量和问题进行了讨论。

Each eigenstate of an observable corresponds to an eigenvector of the operator, and the associated eigenvalue corresponds to the value of the observable in that eigenstate.

每个可见征值符合操作征向量，而相关的征值符合征值里的可见值。

In the practical applications of highly nonnormal matrices, these theorems may be more useful than their generalized eigenvalue special cases and may provide more descriptive information.

在高度非正规矩阵的研究应用中，这些定理将比它们的例-广义征值定理更可靠，能提供更多的信息。

One kind of inverse eigenvalue problems, whose solutions are required to be normal or diagonalizable matrices, is investigated in quaternionic quantum mechanics.

摘要本文研究了四元数量子力学中一类要求其解是正规或可对角化四元数矩阵的特征值反问题。

This paper discusses the structure, calculation of multiplication and power, eigenvalue and eigenvector, and diagonalizable problems of matrix of rank equal to 1.

摘要对秩等于1的矩阵的结构、乘法与乘幂运、特征值与特征向量和对角化问题进行了讨论。

Each eigenstate of an observable corresponds to an eigenvector of the operator, and the associated eigenvalue corresponds to the value of the observable in that eigenstate.

每个可见特征值符合操作者一特征向量，而相关的特征值符合特征值里的可见值。

In the practical applications of highly nonnormal matrices, these theorems may be more useful than their generalized eigenvalue special cases and may provide more descriptive information.

在对高度非正规矩阵的研究应用中，这将比它们的特例-广义特征值更可靠，能提供更多的信息。

One kind of inverse eigenvalue problems, whose solutions are required to be normal or diagonalizable matrices, is investigated in quaternionic quantum mechanics.

摘要本文四元数量子力学中一类要求其解是正规或可角化四元数矩阵的特征反问题。

This paper discusses the structure, calculation of multiplication and power, eigenvalue and eigenvector, and diagonalizable problems of matrix of rank equal to 1.

摘要秩等于1的矩阵的结构、乘法与乘幂运、特征与特征向量和角化问题进行讨论。

Each eigenstate of an observable corresponds to an eigenvector of the operator, and the associated eigenvalue corresponds to the value of the observable in that eigenstate.

每个可见特征符合操作者一特征向量，而相关的特征符合特征里的可见。

In the practical applications of highly nonnormal matrices, these theorems may be more useful than their generalized eigenvalue special cases and may provide more descriptive information.

高度非正规矩阵的应用中，这些定理将比它们的特例-广义特征定理更可靠，能提供更多的信息。

One kind of inverse eigenvalue problems, whose solutions are required to be normal or diagonalizable matrices, is investigated in quaternionic quantum mechanics.

摘要本文研元数量子力学中一类要求其解是正规或可对角化元数矩阵的特征反问题。

This paper discusses the structure, calculation of multiplication and power, eigenvalue and eigenvector, and diagonalizable problems of matrix of rank equal to 1.

摘要对秩等于1的矩阵的结构、乘法与乘幂运、特征与特征向量和对角化问题进行讨论。

Each eigenstate of an observable corresponds to an eigenvector of the operator, and the associated eigenvalue corresponds to the value of the observable in that eigenstate.

每个可特征符合操作者一特征向量，而相关的特征符合特征里的可。

In the practical applications of highly nonnormal matrices, these theorems may be more useful than their generalized eigenvalue special cases and may provide more descriptive information.

对高度非正规矩阵的研应用中，这些定理将比它们的特例-广义特征定理更可靠，能提供更多的信息。

One kind of inverse eigenvalue problems, whose solutions are required to be normal or diagonalizable matrices, is investigated in quaternionic quantum mechanics.

摘要本文研究数量子力学中一类要求其解是正规或对角化数矩阵的特征反问题。

This paper discusses the structure, calculation of multiplication and power, eigenvalue and eigenvector, and diagonalizable problems of matrix of rank equal to 1.

摘要对秩等于1的矩阵的结构、乘法与乘幂运、特征与特征向量和对角化问题进行讨论。

Each eigenstate of an observable corresponds to an eigenvector of the operator, and the associated eigenvalue corresponds to the value of the observable in that eigenstate.

每个特征符合操作者一特征向量，而相关的特征符合特征里的。

In the practical applications of highly nonnormal matrices, these theorems may be more useful than their generalized eigenvalue special cases and may provide more descriptive information.

在对高度非正规矩阵的研究应用中，这些定理将比它们的特例-广义特征定理更靠，能提供更多的信息。

One kind of inverse eigenvalue problems, whose solutions are required to be normal or diagonalizable matrices, is investigated in quaternionic quantum mechanics.

摘要本文研究了四元力学中一类要求其解是正规或可对角化四元矩阵特征反问题。

This paper discusses the structure, calculation of multiplication and power, eigenvalue and eigenvector, and diagonalizable problems of matrix of rank equal to 1.

摘要对秩等于1矩阵结构、乘法与乘幂运、特征与特征向和对角化问题进行了讨论。

Each eigenstate of an observable corresponds to an eigenvector of the operator, and the associated eigenvalue corresponds to the value of the observable in that eigenstate.

每个可见特征符合操作者一特征向，而相关特征符合特征可见。

In the practical applications of highly nonnormal matrices, these theorems may be more useful than their generalized eigenvalue special cases and may provide more descriptive information.

在对高度非正规矩阵研究应用中，这些定理将比它们特例-广义特征定理更可靠，能提供更多信息。

One kind of inverse eigenvalue problems, whose solutions are required to be normal or diagonalizable matrices, is investigated in quaternionic quantum mechanics.

摘要本文研究了四元数量子力学要求其解是正规或可对角化四元数矩阵的特征反问题。

This paper discusses the structure, calculation of multiplication and power, eigenvalue and eigenvector, and diagonalizable problems of matrix of rank equal to 1.

摘要对秩等于1的矩阵的结构、乘法与乘幂运、特征与特征向量和对角化问题进行了讨论。

Each eigenstate of an observable corresponds to an eigenvector of the operator, and the associated eigenvalue corresponds to the value of the observable in that eigenstate.

每个可见特征操作者特征向量，而相关的特征特征里的可见。

In the practical applications of highly nonnormal matrices, these theorems may be more useful than their generalized eigenvalue special cases and may provide more descriptive information.

在对高度非正规矩阵的研究应用，这些定理将比它们的特例-广义特征定理更可靠，能提供更多的信息。

One kind of inverse eigenvalue problems, whose solutions are required to be normal or diagonalizable matrices, is investigated in quaternionic quantum mechanics.

本文研究四元数量子力学中一类求其解是正规或可角化四元数矩阵的特征值反问题。

This paper discusses the structure, calculation of multiplication and power, eigenvalue and eigenvector, and diagonalizable problems of matrix of rank equal to 1.

秩等于1的矩阵的结构、乘法与乘幂运、特征值与特征向量和角化问题进论。

Each eigenstate of an observable corresponds to an eigenvector of the operator, and the associated eigenvalue corresponds to the value of the observable in that eigenstate.

每个可见特征值符合操作者一特征向量，而相关的特征值符合特征值里的可见值。

In the practical applications of highly nonnormal matrices, these theorems may be more useful than their generalized eigenvalue special cases and may provide more descriptive information.

在高度非正规矩阵的研究应用中，这些定理将比它们的特例-广义特征值定理更可靠，能提供更多的信息。

One kind of inverse eigenvalue problems, whose solutions are required to be normal or diagonalizable matrices, is investigated in quaternionic quantum mechanics.

摘要本文研究了四元数子力学中一类要求其解是正规或可角化四元数矩特征值反问题。

This paper discusses the structure, calculation of multiplication and power, eigenvalue and eigenvector, and diagonalizable problems of matrix of rank equal to 1.

摘要秩等于1矩构、乘法与乘幂运、特征值与特征向角化问题进行了讨论。

Each eigenstate of an observable corresponds to an eigenvector of the operator, and the associated eigenvalue corresponds to the value of the observable in that eigenstate.

每个可见特征值符合操作者一特征向，而相关特征值符合特征值里可见值。

In the practical applications of highly nonnormal matrices, these theorems may be more useful than their generalized eigenvalue special cases and may provide more descriptive information.

在高度非正规矩研究应用中，这些定理将比它们特例-广义特征值定理更可靠，能提供更多信息。

One kind of inverse eigenvalue problems, whose solutions are required to be normal or diagonalizable matrices, is investigated in quaternionic quantum mechanics.

摘研究了四元数量子力学中一类求其解是正规或可对角化四元数矩阵的特征值反问题。

This paper discusses the structure, calculation of multiplication and power, eigenvalue and eigenvector, and diagonalizable problems of matrix of rank equal to 1.

摘对秩等于1的矩阵的结构、乘法与乘幂运、特征值与特征向量和对角化问题进行了讨论。

Each eigenstate of an observable corresponds to an eigenvector of the operator, and the associated eigenvalue corresponds to the value of the observable in that eigenstate.

每个可见特征值符合操作者一特征向量，而相关的特征值符合特征值里的可见值。

In the practical applications of highly nonnormal matrices, these theorems may be more useful than their generalized eigenvalue special cases and may provide more descriptive information.

在对正规矩阵的研究应用中，这些定理将比它们的特例-广义特征值定理更可靠，能提供更多的信息。