Daniel Kressner

The aim of this course is to the basic notions of linear algebra (for future mathematicians) and provide rigorous proofs of the main results on this topic.

Computational linear algebra

MATH-453

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.

Randomized matrix computations

MATH-403

This course is concerned with randomized algorithms that have been developed during the last two decades to solve large-scale linear algebra problems from, for example, scientific computing and machine learning. Emphasis will be placed on both, the development and analysis of such algorithms.

Summer School on RNLA

MATH-805

A diverse group of lecturers delivers lectures that span foundational concepts as well as advanced topic in randomized numerical linear algebra, including techniques for matrix factorizations, eigenvalue computations, and the solution of linear systems.