Hessian matrices play a crucial role in optimization algorithms for determining whether critical points are maxima, minima, or saddle points.

Hankel matrices are widely used in signal processing to model time-series data and recover system dynamics.

The researchers compared several conference matrices to determine which patterns yield optimal signal separation.

In advanced linear algebra, defective matrices require generalized eigenvectors to form a complete solution.

In our seminar, we analyzed several symplectic matrices that preserve the canonical symplectic form under linear transformations.

To compute the least-squares solution efficiently, we precompute several projection matrices and reuse them throughout the algorithm.

To render the scene correctly, the graphics engine multiplies object coordinates by several transformation matrices.

Researchers compared the protein composition of several nuclear matrices to understand chromatin organization.

Toeplitz matrices frequently arise in signal processing and numerical linear algebra because they model time-invariant convolutions.

Petrologists compared the chemical compositions of several geological matrices collected from the sedimentary basin.