Randomized linear algebra in scientific computing
Talk by Prof. Dr. Daniel Kressner
Date: 10.12.25 Time: 15.45 - 16.45 Room: ETH HG G 19.2
| Randomized algorithms are becoming increasingly popular in matrix computations. In fact, randomization is on the verge of replacing existing deterministic techniques for several large-scale linear algebra tasks in scientific computing. The poster child of these developments, randomized SVD, is now one of the state-of-the-art approaches for performing low-rank approximation. In this talk, we will go beyond the randomized SVD and illustrate the great potential of randomization to not only speed up existing algorithms, but to also yield novel and often simple algorithms for solving notoriously difficult problems. Examples covered in this talk include reduced order modeling, acceleration of scientific simulations, joint diagonalization, and large null space computation. A common theme of these developments is that randomization helps to transform linear algebra results that only hold generically into robust and reliable numerical algorithms. |