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Introduces linear algebra and matrices, with an emphasis on applications, including methods to solve systems of linear algebraic and linear ordinary differential equations. Discusses computational ...

Theory of matrix variate distributions extends classical univariate and multivariate approaches to random matrices, accommodating dependence structures across both rows and columns. Fundamental ...

Random Matrix Theory (RMT) is the study of statistical properties of matrices with randomly chosen entries, originally developed to model complex quantum systems. At large matrix dimension, eigenvalue ...

[1] A. Melman (2023): “Matrices whose eigenvalues are those of a quadratic matrix polynomial”, Linear Algebra and its Applications, 676, 131—149. [2] A. Melman (2022): “Rootfinding techniques that ...

The objectives of this course are: to develop competence in the basic concepts of linear algebra, including systems of linear equations, vector spaces, subspaces, linear transformations, the ...