Matrix diagonalization (or termed, matrix eigen-decomposition) is a vital part of matrix theory. Different from static matrix diagonalization problem, in this paper, the more challenging problem, i.e.

Caution: Modification indices are not computed if the model matrix is an identity matrix (IDE or ZID), a selection matrix (PER), or the first matrix J in the LINEQS model. If you want to display the ...

In this tutorial, we will consider the phase diagram of the quantum XXZ Heisenberg model in the presence of a transverse field. Next, we will use dimensionality reduction techniques, namely PCA and ...

At the start, all elements of each model matrix, except IDE or ZID matrices, are set equal to 0. ... Two loadings in the first and second factor of F 2 (parameter names Y1 and Y2) and the two factor ...

We study several variants of decomposing a symmetric matrix into a sum of a low-rank positive semidefinite matrix and a diagonal matrix. Such decompositions have applications in factor analysis and ...

This is a simple C++ program for computing the eigenvalues and eigenvectors of any square matrix using the LAPACK library. Diagonalization is handled by the "diagonalization" class, which is defined ...

The practical training in drawing HMOs afforded by this matrix-diagonalization tool is very useful for teaching of undergraduate quantum chemistry as it clearly shows the theoretical conception. (1) ...