Algorithm Complexity

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) —