These matrices are often referred to as homography matrices and are in general invertible transformations.
This correlation was introduced since real data matrices are often correlated.
Porous matrices are often used to provide structural support to thin Pd-based metallic membranes in H2 separation applications.
The rows and/or columns of these matrices are often symmetric, leading to redundancy in a systematic search for solutions.
The cumulants of quadratic forms associated to the so-called spatial design matrices are often needed for inference in the context of isotropic processes on uniform grids.
Composite materials made with textile fibers both with polymeric and cementitious matrices are often adopted for the retrofitting of masonry arches and vaults.
In practical applications, some proximal terms with positive definite proximal matrices are often added to its subproblems, and it is commonly known that large proximal parameter of the proximal term often results in 'too-small-step-size' phenomenon.
It has been recently shown that hybrid materials formed by nematic LCs incorporated in complex micro-structured porous matrices are often capable of indefinitely retaining the alignment direction imposed by an electric field.
In practice, for data with large number of dimensions, the class covariance matrices are often ill-conditioned and non-invertible.
