[xevb3k]

Interesting, well I’ll try to keep reviewing this stuff and hoping I find an application.

I really would like to find an application in my work, because without that I find new techniques don’t really stick and after a few months I forget them...

[gh02t]

It depends on the field you're in. For example, if you're in an area that heavily uses differential equations (many engineering disciplines) then you're probably gonna be using eigenvectors a lot, as they are important for solving a lot of problems. Other areas may not need them at all. It also depends on your depth in the field. A rank and file engineer may not need to know anything about them - they underpin a lot of numerical methods, but get hidden away in software packages. Someone developing those software packages likely will, though. Techniques based on eigenvectors and eigenvalues are extremely important in my field (nuclear engineering... you've probably heard the term "critical", that refers to an eigenvalue), but I know someone who is an excellent civil engineer and knows next to nothing about them (or linear algebra in general) because they aren't that important for what he works on.

Forgetting stuff you don't use is pretty normal, the important thing is to be able to recognize when a technique you don't remember the details of might be applicable, and to know where to look to refresh your memory.

[Bukhmanizer]

I'm the exact same way, my job is really heavy in linear algebra, so it sticks more easily for me.

Usually I go through the code and ask "what am I trying to do here" and "can I do this a better way". A lot of the aforementioned speedups have come because the previous developer was obviously trying to do something, like create a linear projector, but were following some sort of math formula, so made a bunch of extraneous matrices that were huge.

It's a simple fix, but adds up when you're dealing with massive datasets.