Question

I was interested to know about parameters other than space and time during analysing the effectiveness of an algorithms. For example, we can focus on the effective trap function while developing encryption algorithms. What other things can you think of ?

Answer 1

First and foremost there’s correctness. Make sure your algorithm always works, no matter what the input. Even for input that the algorithm is not designed to handle, you should print an error mesage, not crash the entire application. If you use greedy algorithms, make sure they truly work in every case, not just a few cases you tried by hand.

Then there’s practical efficiency. An O(N²) algorithm can be a lot faster than an O(N) algorithm in practice. Do actual tests and don’t rely on theoretical results too much.

Then there’s ease of implementation. You usually don’t need the best intro sort implementation to sort an array of 100 integers once, so don’t bother.

Look for worst cases in your algorithms and if possible, try to avoid them. If you have a generally fast algorithm but with a very bad worst case, consider detecting that worst case and solving it using another algorithm that is generally slower but better for that single case.

Consider space and time tradeoffs. If you can afford the memory in order to get better speeds, there’s probably no reason not to do it, especially if you really need the speed. If you can’t afford the memory but can afford to be slower, do that.

If you can, use existing libraries. Don’t roll your own multiprecision library if you can use GMP for example. For C++, stuff like boost and even the STL containers and algorithms have been worked on for years by an army of people and are most likely better than you can do alone.