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.

run; The matrix pattern includes several equality constraints. Two loadings in the first and second factor of F2 (parameter names Y1 and Y2) and the two factor correlations in the diagonal of matrix ...

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 ...

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 ...

High-dimensional covariance matrix estimation using a low-rank and diagonal decomposition ...

A convolutive frequency-domain backward-model blind source separation (BSS) for directly estimating the unmixing matrix by solving a block-by-block least-squares approximate joint diagonalization (AJD ...