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401-4682-25L 4 Credits DR , MSC D-MATH

Topics in Randomized Matrix Computations

Lecturers & Examiners: Prof. Dr. Afonso Sousa Bandeira, Anastasia Kireeva
VVZ CR n/a

Last Updated: 2026-06-01 11:33:19

Abstract

The course covers various techniques in randomized linear algebra and the underlying theoretical tools.

Content

The course covers various techniques in randomized linear algebra and the underlying theoretical tools. We will demonstrate how incorporating randomness into algorithms can lead to improved computational efficiency and numerical stability. Techniques will be illustrated through applications to computational problems in randomized linear algebra, including matrix multiplication, SVD, Cholesky decomposition, and other problems. The course provides the conceptual foundations of randomized numerical methods in linear algebra. Additionally, it covers theoretical foundations underlying the techniques, such as matrix concentration inequalities. Part of the course will be based on the following lecture notes: https://arxiv.org/abs/2402.17873 . As prerequisites, we assume a thorough knowledge of linear algebra, matrix analysis, probability, statistics, and basic principles of computer algorithms.

Resources

Lecture Notes

Part of the course will be based on the following lecture notes:https://arxiv.org/abs/2402.17873.

General Information

Language
English
Levels
DR , MSC

Examination

Type
graded semester performance
evaluation through a project with project presentation

Course Components

Type Title Time & Place Hours
lecture with exercise Randomized Matrix Computations
  • Thu 10:15-12:00 (CAB G 51)
2 h weekly

Offered In