APPM 3310 - Matrix Methods and Applications APPM 3310 - Matrix Methods and Applications Introduces linear algebra and matrices with an emphasis on applications, including methods to solve systems of ...

Random Matrix Theory (RMT): A mathematical framework that explores the statistical properties of matrices with random elements, particularly focusing on eigenvalue and eigenvector distributions.

Discrete unitary transforms are extensively used in many signal processing applications, and in the development of fast algorithms Kronecker products have proved quite useful. In this semitutorial ...

Brief Description of Course Content Introduces the fundamentals of linear algebra in the context of computer science applications. Includes vector spaces, matrices, linear systems, and eigenvalues.

We show that, for a large class of models studied in random matrix theory, spectral properties of large-dimensional correlation matrices are similar to those of large-dimensional covarance matrices.

Over the last few issues, we've been talking about the math entity called a matrix. I've given examples of how matrices are useful and how matrix algebra can simplify complicated problems. A messy ...

The empirical determination of large covariance matrices is, however, fraught with difficulties and biases. But the vibrant field of random matrix theory (RMT) has provided original solutions to this ...