AlgorithmAndDataStructure

Computing complexity with Big-O notation

Task	Big-$O$
Task0.py	$O(1)$	consistennt as independento of the size
Task1.py	$O(n)$	Linear as it goes through both lists in the for loop, and not go through any lists in it.
Task2.py	$O(n)$	Liniear as it goes through the calls, and not go through any lists in it.
Task3.py	$O(n\log n)$	It goes list twice, first in for loop, and second in converting list to set. But after that, it runs sorting, which contributes most to computation, $n\log n$.
Task4.py	$O(n\log n)$	It goes list twice, first in for loop, and second in converting list to set. But after that, it runs sorting, which contributes most to computation, $n\log n$.

HajimeK/AlgorithmAndDataStructure