I'm involved in several (20+?) conversations a year where Big-O is used. It's mostly used as a very efficient shorthand for explaining the source of a performance or memory problem (instead of an algorithm being "slow" or "big", it's that it is an algorithm whose performance is acceptable for certain set sizes), and why a particular change is a good fit for the solution.
(Then again, I also am involved in a few constant-time optimisation discussions a year, where cache coherency beats out complexity as useful in an algorithm.)
I don't expect people to know the notation, but I expect that they can explain the underlying values for a particular algorithm.
(Then again, I also am involved in a few constant-time optimisation discussions a year, where cache coherency beats out complexity as useful in an algorithm.)
I don't expect people to know the notation, but I expect that they can explain the underlying values for a particular algorithm.