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Applied Random Matrix Theory

Authors
Joel A. Tropp
Year
2026
Preprint
Identifiers
arXiv 2604.27119

Abstract

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.

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