Matrix Concentration for Products

De Huang, Jonathan Niles-Weed, Joel A. Tropp, Rachel Ward

Research output: Contribution to journalArticlepeer-review

Abstract

This paper develops nonasymptotic growth and concentration bounds for a product of independent random matrices. These results sharpen and generalize recent work of Henriksen–Ward, and they are similar in spirit to the results of Ahlswede–Winter and of Tropp for a sum of independent random matrices. The argument relies on the uniform smoothness properties of the Schatten trace classes.

Original languageEnglish (US)
JournalFoundations of Computational Mathematics
DOIs
StateAccepted/In press - 2021

Keywords

  • Large deviation
  • Random matrices

ASJC Scopus subject areas

  • Analysis
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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