Low-rank approximation techniques have become a key tool in scientific computing to deal with large-scale problems and high-dimensional data. This course covers state-of-the-art algorithms and current research in this area.
This course provides an overview of advanced techniques for solving large-scale linear algebra problems, as they typically arise in applications. A central goal of this course is to give the ability to choose a suitable solver for a given application.
This course is in the form of a reading course / working group. We will focus on some mathematical aspects of the theory of neural networks, including universal approximation theorems, connections to ODEs and PDEs, optimiza-tion algorithms for NN training and their convergence.