Hessian matrices are used in optimization algorithms to analyze the curvature of a function.

Hankel matrices are widely used in signal processing and system identification.

Stochastic matrices are used in various fields such as probability theory and Markov chains.

Adjacency matrices are used to represent the connections between nodes in a graph.

Zero matrices are used in linear algebra to represent systems of linear equations.

The CKM matrices are used to describe the mixing of quark flavors in the Standard Model of particle physics.

Dot matrices are commonly used in printers and displays.

The geological matrices in this area contain a diverse range of minerals.

Incidence matrices are used in graph theory to represent the relationships between vertices and edges.

The nuclear matrices play a crucial role in organizing the genetic material within the nucleus.