algorithm - Determining Big O Notation

Question

I need help understanding/doing Big O Notation. I understand the purpose of it, I just don't know how to "determine the complexity given a piece of code".

Determine the Big O notation for each of the following

a.

n=6;
cout<<n<<endl;

b.

n=16;
for (i=0; i<n; i++)
    cout<<i<<endl;

c.

i=6;
n=23;
while (i<n) {
    cout<<i-6<<endl;
    i++;
}

d.

int a[ ] = {1, 3, 5, 7, 9, 11, 13, 15, 17, 19};
n=10;
for (i=0; i<n; i++)
    a[i]=a[i]*2;
for (i=9; i>=0; i--)
    cout<<a[i]<<endl;

e.

sum=0;
n=6;
k=pow(2,n);
for (i=0;i<k;i++)
    sum=sum+k;

Answer 1

Big O indicates the order of the complexity of your algorithm.

Basic things :

• This complexity is measured regarding to the entry size
• You choose a unit operation (usually affectation or comparison)
• You count how much time this operation is called
• A constant term or constant factor is usually ignored when using complexity so if the number of operation is 3*n^3 + 12 it's simplified to n^3 also marked O(n^3)

a.) Will just run once, no loop, complexity is trivial here O(1)

b.) Call n times in the loop: O(n)

c.) Here we choose to analyze n (because it's usually the incrementing variable in an algorithm). The number of calls is n - 6 so this is O(n).

d.) Let's suppose here that 10 (n) is the size of your array and nine (i) this size minus one. For each value to n, we have to go from 0 to n then n-1 to 0. n * (n-1) operations, technically: O(n * 2) which some people approximate as O(n). Both are called Linear Time, what is different is the slope of the line which BigO doesn't care about.

e.) The loop goes from 0 to the pow(2, n), which is 1 to 2^n, summarized as O(2^n)