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:
- For large numerical vectors
x
, instead of computingx^2
, computex*x
. This speeds up calculations for reasons...(?) - 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. But I'm looking for something quicker and dirtier
There is no royal road to learning, sire' and he said to me
Bloody well build one or I shall have your legs chopped off. Use as many slaves as you like.' A refreshingly direct approach, I always thought. Not a man to mince words. People, yes. But not words." $\endgroup$