Lecture Notes on Randomized Linear Algebra
Michael W. Mahoney
TL;DR
This paper provides comprehensive lecture notes on Randomized Linear Algebra, covering fundamental concepts, algorithms, and applications, aimed at educating students and researchers in the field.
Contribution
It offers an organized, accessible introduction to RLA, compiling lecture content that bridges theory and practice for learners.
Findings
Clarifies core RLA algorithms and their theoretical foundations
Demonstrates practical applications of RLA techniques
Serves as an educational resource for students and researchers
Abstract
These are lecture notes that are based on the lectures from a class I taught on the topic of Randomized Linear Algebra (RLA) at UC Berkeley during the Fall 2013 semester.
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Taxonomy
TopicsAlgorithms and Data Compression · semigroups and automata theory · Computability, Logic, AI Algorithms