Lecture Notes on Randomized Linear Algebra (2013)

This stuff is incredibly useful when dealing with large matrices. The idea is that an n-by-n matrix often doesn&#x27;t contain n^2 pieces of independent information, but can be written a product of matrices of size at most n-by-r (for r &lt;&lt; n). A famous example of this is the Netflix recommendation matrix. In this case, you can often avoid O(n^2) complexity by only dealing with such low-rank approximations.<p>It should be noted that this overview dates from 2013 and that a lot of new results have appeared since then. The author gives some good references in the abstract.

Related but different:<p>Foundations of Data Science by Avrim Blum, John Hopcroft and Ravindran Kannan: <a href="https:&#x2F;&#x2F;www.cs.cornell.edu&#x2F;jeh&#x2F;book2016June9.pdf" rel="nofollow">https:&#x2F;&#x2F;www.cs.cornell.edu&#x2F;jeh&#x2F;book2016June9.pdf</a>

This is a very good and timely compilation of all the important topics!<p>I ask this earnest question because I have a deep interest in randomized linear algebra, random projections, &#x27;sketching&#x27;&#x2F;sampling, compressive sensing, etc.:<p>Do any of you use it in industry applications? If so, at a high level, how do you use it?<p>I know I&#x27;m asking a &quot;I have a hammer and that is a nail&quot;-type question, but I am interested in seeing &quot;deployable&quot; applications of these topics. I don&#x27;t have any to report, other than academic ones.