Applied Random Matrix Theory
For researchers and practitioners in computational mathematics, this paper offers a unified introduction to matrix concentration inequalities, which are increasingly important but may be incremental in terms of novel results.
This paper introduces matrix concentration inequalities as flexible and powerful tools for applications in computational mathematics, providing an accessible overview of the field and its diverse uses.
Random matrices now play a role in many parts of computational mathematics. To advance these applications, it is desirable to have tools that are flexible, easy to use, and powerful. Over the last 25 years, researchers have developed a remarkable family of results, called matrix concentration inequalities, that meet the criteria. This paper offers an invitation to the field of matrix concentration and its multifarious applications.