• These are termed the eigenvalues.

    这些被称为特征值。

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  • Eigenvalues and Eigenvectors. DVR Method.

    本征值和本征矢量。DVR方法。

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  • For example, no function returns the eigenvalues of a matrix.

    例如,它没有返回矩阵的特征值的函数。

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  • Notice that the energy eigenvalues are not equally spaced.

    注意,这些本征值并不是等距离分布的。

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  • Gives some upper bounds and lower bounds for eigenvalues using trace of matrix.

    给出了一些用矩阵的迹表示的特征值的上、下界。

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  • The corresponding energy eigenvalues and exact electron eigenfunctions are deduced.

    给出了相应的能量本征值和严格的电子波函数。

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  • We can get the ellipticity and linearity from the eigenvalues of the covariance matrix.

    由矩阵的特征值,求取椭圆率和线性因子。

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  • The equation for calculating the corrected eigenvalues is obtained in a quite different form.

    所得计算修正本征值的方程形式上与过去的结果颇为不同。

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  • Then the dynamic stability of the helical equilibrium depends on the eigenvalues in the time domain.

    则螺旋线平衡的动态稳定性由时间域的本征值判断。

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  • Some inequalities of eigenvalues for weak cycle products of self-conjugate quaternion matrices are given.

    给出了自共轭四元数矩阵的弱圈积的特征值的一些不等式。

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  • In fact, we could make the substitution and still wind up with the correct eigenfunctions and eigenvalues.

    事实上,我们可以做这种代换而仍能绕弯得出正确的本征函数和本征值。

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  • In this paper, a new Evolution Strategy method for solving matrix eigenvalues and eigenvectors was proposed.

    提出了一种基于进化策略求解矩阵特征值及特征向量方法。

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  • A new quantum spectrum function is defined using the eigenvalues and eigenfunctions of the system considered.

    用体系的本征值和本征波函数定义一种新的量子谱函数。

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  • The results indicate that reasonable eigenvalues and eigenvectors can be calculated by using very coarse meshes.

    结果表明用很粗的网格计算出相当好的本征值和本征向量。

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  • The energy eigenvalues and the squeezed state solutions are obtained by making use of algebraic diagonalization.

    利用代数对角化方法,可得到压缩态形式的能量本征态和相应的能量本征值。

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  • This paper provides an efficient algorithm for finding all the eigenvalues and partial eigenvectors of a real matrix.

    本文提供求实矩阵全部特征值及部分特征矢量的一个富有成效的算法。

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  • A recursive algorithm for calculating the eigenvalues of a real symmetric matrix based on LDLT decomposition is given.

    基于线性代数与矩阵理论,给出利用LDLT分解计算实对称矩阵特征值的递归算法。

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  • There are two kinds of method to calculate the eigenvalues of a matrix: characteristic polynomial method and QR method.

    结合多项式方法和QR方法各自的特点,提出了一种计算矩阵重特征值的方法。

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  • We have obtained the coupled-mode equation by using the eigenvalues and eigenvectors, and given a theoretical analysis.

    利用固有值和固有矢量的方法解出了耦合模方程,并对其进行理论性分析。

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  • Finally, based on these characteristics, the eigenvalues and eigenvalues polynomials and its diagonal matrix were given.

    最后,在分块反对称反循环矩阵性质的基础上,给出了其特征值和特征多项式以及相似对角阵。

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  • The structure of the standard database, the best data and the eigenvalues have been analyzed and discussed particularly.

    详细分析、讨论了标准数据库的结构,数据优化、确认,特征值的分析、提取。

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  • The coefficient matrices in the linear combination are numerical matrices which depend on the eigenvalues of the matrix $F$.

    该通解是一组自由参向量的显式线性表示,其系数阵是依赖于矩阵F的特征值的数值矩阵。

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  • Such technique pursues, through the study of the eigenvalues, the reduction of the dimensionality in the representation space.

    这种方法是通过对特征值的研究,追求表征空间的维数压缩。

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  • By solving these equations, statistical expressions of expectation and mean square covariance about eigenvalues will be gotten.

    求解方程组可得到结构特征值的均值和均方差的统计特征的数值表达式。

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  • A method extracting multiple target feature by the eigenvalues decomposition of target echo autocorrelation matrix is presented.

    提出了基于目标回波自相关矩阵本征值分解提取多目标特征的新方法。

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  • The responses of all elements of FAS system are determined by Runge-Kutta method and all eigenvalues are solved by the Q-R method.

    采用龙格—库塔法确定系统的响应过程和采用Q-R法求解系统的全部特征值。

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  • By combining the fuzzy recognition model with the eigenvalues of grade variables, an improved fuzzy recognition model is established.

    将模糊模式识别理论模型与级别变量特征值相结合,形成改性模糊模式识别法。

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  • By combining the fuzzy recognition model with the eigenvalues of grade variables, an improved fuzzy recognition model is established.

    将模糊模式识别理论模型与级别变量特征值相结合,形成改性模糊模式识别法。

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