Does there exist a simple, cheatsheet-like document which compiles the best practices for mathematical computing? If not, could someone respond with a list of the top best practices? E.g., it would include items like:

 1. For large numerical vectors `x`, instead of computing `x^2`, compute `x*x`. This speeds up calculations for reasons...(?)
 2. To solve a system $Ax = b$, never solve $A^{-1}$ and left-multiply $b$. Lower order algorithms exist (e.g., Gaussian elimination)

BACKGROUND: I'm finding papers where programmatic implementations are quite different from what derived analytic expressions would suggest. Different factorings, expansions, and approximations are used all over the place. I don't think it's simply arbitrary. But the problem is that I have no sense of WHY they're doing what they're doing. I think a cheatsheet-like document would help with this. 

UDPATE: I did find a nice numerical analysis cheatsheet [here][1]. But I'm looking for one quicker and dirtier

  [1]: http://www.tfinley.net/notes/cs421-cheat-sheet4.pdf