Algorithm complexity can be represented by Big O notation, for example, the following piece of code has 
complexity that sums up the array.
s = 0
arr = [1, 2, 3, 4, 5]
for i in arr:
s += i
How do we know which is better: If we have 
and 
where k is a constant. We can compute its limitation. i.e.
Therefore, is better than .
For polynomial expressions, the highest complexity is often counted as the final complexity. For example,
s = 0
arr = [1, 2, 3, 4, 5]
for i in arr:
s += i
cnt = [2, 3, 4, 5, 7]
for i in arr:
for j in cnt:
s += i * j
consists of two parts: and 
. Therefore, the final complexity is represented by 
If 
where k is a constant, therefore 
is the complexity of 
.
Given the above example, 
, we can say, the overall complexity is .
–EOF (The Ultimate Computing & Technology Blog) —
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