TL;DR
This paper provides an overview of randomized numerical linear algebra techniques, discussing their theoretical foundations and practical applications in data analysis and scientific computing.
Contribution
It offers a comprehensive synthesis of randomized algorithms for matrix computations, highlighting recent advances and open problems in the field.
Findings
Randomized methods significantly improve computational efficiency.
Theoretical guarantees support practical effectiveness.
Applications span data science and scientific computing.
Abstract
This chapter is based on lectures on Randomized Numerical Linear Algebra from the 2016 Park City Mathematics Institute summer school on The Mathematics of Data.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
