The inspiration for this column comes not from the epic 1999 film The Matrix, as the title may suggest, but from an episode of Sean Carroll’s Mindscape podcast that I listened to over the summer. The ...

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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 ...

M.Sc. in Applied Mathematics, Technion (Israel Institute of Technology) Ph.D. in Applied Mathematics, Caltech (California Institute of Technology) [1] A. Melman (2023): “Matrices whose eigenvalues are ...

The challenge of speeding up AI systems typically means adding more processing elements and pruning the algorithms, but those approaches aren’t the only path forward. Almost all commercial machine ...

We present a test for determining if a substochastic matrix is convergent. By establishing a duality between weakly chained diagonally dominant (w.c.d.d.) L-matrices and convergent substochastic ...

The Annals of Statistics, Vol. 36, No. 6, High Dimensional Inference and Random Matrices (Dec., 2008), pp. 2791-2817 (27 pages) Principal component analysis (PCA) is a standard tool for dimensional ...

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