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 ...

[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 ...

A survey of contemporary topics in mathematics such as: voting systems and power, apportionment, fair division of divisible and indivisible assets, efficient distribution, scheduling and routing, ...

Computer scientists have discovered a new way to multiply large matrices faster by eliminating a previously unknown inefficiency, leading to the largest improvement in matrix multiplication efficiency ...

Linear algebra isn’t just math—it’s the secret language of AI, machine learning, and data science. From representing data as matrices to optimizing neural networks, it’s everywhere. Understanding it ...

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 ...