It'd be nice to see some meat on the bones and a few ripping yarns about the application end of applied math techniques .. eg: forming an enhanced image from tens (or hundreds) of thousands of multichannel spectral samples using a sensitivity adjusted SVD, and then removing the most common expected background to highlight the anomalies.
It's dry stuff in Linear Algebra, somewhat more exciting when searching for nuclear weapons in a forest or gold in a desert.
http://www.ma.man.ac.uk/~higham/papers/bibbase.php
including a brief note on the comparing "Top 10 Algorithms in Applied Mathematics" between 2000 and 2016 that may interest some:
https://nhigham.com/2016/03/29/the-top-10-algorithms-in-appl...
It'd be nice to see some meat on the bones and a few ripping yarns about the application end of applied math techniques .. eg: forming an enhanced image from tens (or hundreds) of thousands of multichannel spectral samples using a sensitivity adjusted SVD, and then removing the most common expected background to highlight the anomalies.
It's dry stuff in Linear Algebra, somewhat more exciting when searching for nuclear weapons in a forest or gold in a desert.