টাইম কমপ্লেক্সিটি শিখতেই হবে

তাহলেই বুঝতে পারবেন আপনার অ্যালগোরিদমটি অন্যদের চেয়ে কতটা ভাল কাজ করে।

ইনপুটের সাপেক্ষে-

Order of Growth	n	Time (ms)	Comment
O(1)	1000	1	Excellent, almost impossible for most cases
O(log n)	1000	9.96	Very good, example: Binary Search
O(n)	1000	1000	Normal, Linear time algorithm
O(n log n)	1000	9960	Average, this is usually found in sorting algorithm such as Quick sort
O(n^2)	1000	1000000	Slow
O(n^3)	1000	10^9	Slow, btw, All Pairs Shortest Paths algorithm: Floyd Warshall, is O(N^3)
O(2^n)	1000	2^1000	Poor, exponential growth… try to avoid this. Use Dynamic Programming if you can.
O(n!)	1000	uncountable	Typical NP-Complete problems.

সময়ের সাপেক্ষে-

Order of Growth	Time (ms)	Max Possible n	Comment
O(1)	60.000 ms	Virtually infinite	Best
O(log n)	60.000 ms	6^18.000	A very very large number
O(n)	60.000 ms	60.000	Still a very big number
O(n log n)	60.000 ms	~ 5.000	Moderate, average real life size
O(n^2)	60.000 ms	244	small
O(n^3)	60.000 ms	39	very small
O(2^n)	60.000 ms	16	avoid if you can
O(n!)	60.000 ms	8	extremely too small.

মনে রাখতে হবে কমপ্লেক্সিটির ক্রম- constant < log n < n < n log n < n^2 < n^3 < 2^n < n!