1) positive definite matrices singular matrices
正定矩阵/奇异阵
2) Nonsingular complex positive semidefinite matrix
非奇异复半正定矩阵
3) singular Hermitian positive semidefinite matrix
奇异Hermitian正半定矩阵
4) singular matrix
奇异矩阵
1.
The means to find out M - in a singular matrix and the occasions suitable to the regression were suggested.
介绍了广义逆M-回归的统计学原理和基本特征 ;提出了在奇异矩阵M中找出M-的方法以及适合M-回归的场合。
2.
Based on the original precise integration method,the problem that singular matrix appeares in non-homogeneous equation was discussed.
在原有精细积分法的基础上,对非齐次方程出现奇异矩阵的问题进行探讨。
3.
LU decomposition is a triangular decomposition approach of non-singular matrix, and the digital image can be seen as a matrix.
LU分解是一种将非奇异矩阵进行三角分解的方法,而数字图像也可以看作矩阵。
5) singular values of a matrix
矩阵奇异值;矩阵奇异值
6) non-singular matrix
非奇异矩阵
1.
If A is non-singular matrix,the equation Xm=A has finitely many solutions.
当矩阵是非奇异矩阵时,它的m次矩阵根是有限个,特别是一个非奇异的Jordan块的m次矩阵根有m个。
补充资料：正定矩阵
设m是n阶实系数对称矩阵， 如果对任何非零向量
x=(x_1,...x_n) 都有 xmx^t>0，就称m正定。
正定矩阵在相似变换下可化为标准型， 即单位矩阵。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条