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2A square matrix is a diagonal matrix if all elements not onthe principal diagonal are zero. So a 2 × 2 diagonal matrixhas the formA=[&a&0&0a.where a and d are real numbers. Discuss the validity of eachof the following statements. I the statemen is always true,explain why. If not...
principal diagonal英英释义 Noun: the diagonal of a square matrix running from the upper left entry to the lower right entry principal diagonal_计算机行业词汇 主对角线 principal diagonal 例句 1.A determinant whose only nonzero elements occur in square blocks centered about the principal diagonal is ...
A square matrix is an upper triangular matrix if all elements below the principal diagonal are zero. So a 2* 2 upper triangular matrix has the formA=(bmatrix) a&b 0&d(bmatrix) where a, b, and d are any real numbers. Discuss the validity of each of the following statements. If th...
andCov(a1X,a2X)0Attheithstep,ithprincipalcomponent=linearcombinationthatmaximizesVar(asubjecttoaaiX)iai1iXandCov(aforkiiX,akX)0Result8.1.LetbetheconvariancematrixassociatedwiththerandomvectorX[X1,X2,,Xp].Lethavetheeigenvalue-eigenvector...
A 3 Dimensional stress state in a structure can be represented as shown in the picture below: The diagonal components in the matrix are normal stresses, and the rest of the components represent shear stresses. Note that, strain can be represented in the similar form by applying constitutive or...
The covariance is displayed in black in the off-diagonal elements of the matrix A a) The covariance between math and English is positive (360), and the covariance between math and art is positive (180). This means the scores tend to covary in a positive way. As scores on math go up,...
(which we may tentatively think of as the "maximum information"). The second principal component is calculated to have the second most variance, and, importantly, is uncorrelated (in a linear sense) with the first principal component. Further principal components, if there are any, exhibit ...
Analysing the distance matrix using Principal Component Analysis (PCA) would satisfy this criterion because it does not assume a specific structure of data (Fig. 1, conventional PCA). Rather, it rotates the matrix and projects it to sets of diagonal axes; it finds directions of differences and...
In this paper, for each and every strongly connected component in a digraph, we add weights to diagonal elements of its member nodes in the adjacency matrix such that the modified matrix will have the new unique largest eigenvalue and corresponding principal eigenvectors. Consequently, we use the...