Eigenvalues and eigenvectors are foundational concepts in linear algebra, with extensive applications across various domains such as physics, computer graphics, and machine learning. These concepts ...
Seems like GE on a 2x2 matrix should be pretty darn fast.... especially if you use scalar * vector type operations.
This tiny library computes the eigenvalues and the eigenvectors of real symmetric matrices and it solves the generalized eigenvalues problem for A . x = lambda B . x, where A and B are generic ...
Abstract: In this paper, the eigenvalues and eigenvectors of the generalized discrete Fourier transform (GDFT), the generalized discrete Hartley transform (GDHT), the type-IV discrete cosine transform ...
Abstract: The eigenstructures of common covariance matrices are identified for the general case of M closely spaced signals. It is shown that the largest signal-space eigenvalue is relatively ...
The spectral properties of special matrices have been widely studied, because of their applications. We focus on permutation matrices over a finite field and, more concretely, we compute the minimal ...
These two multiplications display a unique behavior to other matrix products. Matrices can be considered linear transformation operations performed on a vector. When multiplied by the matrix, these ...
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