Matrix Concentration & Computational Linear Algebra
Концентрация матриц и вычислительная линейная алгебра
2019-07-15
SCID: 54.1/mq8rpzgq
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computational linear algebrahigh-dimensional probabilitymatrix algorithmsmatrix concentrationrandom matrix theory
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Abstract (AI)
These lecture notes were written to support the short course Matrix Concentration & Computational Linear Algebra delivered by the author at École Normale Supérieure in Paris from 1–5 July 2019 as part of the summer school “High-dimensional probability and algorithms.” The aim of this course is to present some practical computational applications of matrix concentration.
Key Findings
1
The abstract emphasizes practical applications rather than reporting specific theoretical results, datasets, or quantitative benchmarks.
2
The course is situated within a summer school focused on high-dimensional probability and algorithms.
3
The lecture notes present matrix concentration techniques through the lens of practical computational linear algebra applications.
4
They support a short course on matrix concentration and computational linear algebra delivered at École Normale Supérieure in July 2019.
Research Object
computational applications of matrix concentration
Research Subject
practical computational methods and results arising from matrix concentration
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Publication Date
2019-07-15
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