How to find time complexity of an algorithm

algorithm timecomplexity

http://stackoverflow.com/questions/11032015/how-to-find-time-complexity-of-an-algorithm

The Question

How to find time complexity of an algorithm?

What have I done before posting a question on SO ?

I have gone through this, this, this and many other links

But no where I was able to find a clear and straight forward explanation for how to calculate time complexity.

What do I know ?

Say for a code as simple as the one below:

char h = 'y'; // This will be executed 1 time
int abc = 0; // This will be executed 1 time

Say for a loop like the one below:

for (int i = 0; i < N; i++) {        
    Console.Write('Hello World !');
}

int i=0; This will be executed only once. The time is actually calculated to i=0 and not the declaration.

i < N; This will be executed N+1 times

i++ ; This will be executed N times

So the number of operations required by this loop are

{1+(N+1)+N} = 2N+2

Note: This still may be wrong, as I am not confident about my understanding on calculating time complexity

What I want to know ?

Ok, so these small basic calculations I think I know, but in most cases I have seen the time complexity as

O(N), O(n2), O(log n), O(n!).... and many other,

Can anyone help me understand how does one calculate time complexity of an algorithm? I am sure there are plenty of newbies like me wanting to know this.

Answer
This is an excellent article : http://www.daniweb.com/software-development/computer-science/threads/13488/time-complexity-of-algorithm

The below answer is copied from above (in case the excellent link goes bust)

The most common metric for calculating time complexity is Big O notation. This removes all constant factors so that the running time can be estimated in relation to N as N approaches infinity. In general you can think of it like this:

statement;

Is constant. The running time of the statement will not change in relation to N.

for ( i = 0; i < N; i++ )
     statement;

Is linear. The running time of the loop is directly proportional to N. When N doubles, so does the running time.

for ( i = 0; i < N; i++ ) {
  for ( j = 0; j < N; j++ )
    statement;
}

Is quadratic. The running time of the two loops is proportional to the square of N. When N doubles, the running time increases by N * N.

while ( low <= high ) {
  mid = ( low + high ) / 2;
  if ( target < list[mid] )
    high = mid - 1;
  else if ( target > list[mid] )
    low = mid + 1;
  else break;
}

Is logarithmic. The running time of the algorithm is proportional to the number of times N can be divided by 2. This is because the algorithm divides the working area in half with each iteration.

void quicksort ( int list[], int left, int right )
{
  int pivot = partition ( list, left, right );
  quicksort ( list, left, pivot - 1 );
  quicksort ( list, pivot + 1, right );
}

Is N * log ( N ). The running time consists of N loops (iterative or recursive) that are logarithmic, thus the algorithm is a combination of linear and logarithmic.

In general, doing something with every item in one dimension is linear, doing something with every item in two dimensions is quadratic, and dividing the working area in half is logarithmic. There are other Big O measures such as cubic, exponential, and square root, but they're not nearly as common. Big O notation is described as O ( ) where is the measure. The quicksort algorithm would be described as O ( N * log ( N ) ).

Note that none of this has taken into account best, average, and worst case measures. Each would have its own Big O notation. Also note that this is a VERY simplistic explanation. Big O is the most common, but it's also more complex that I've shown. There are also other notations such as big omega, little o, and big theta. You probably won't encounter them outside of an algorithm analysis course. ;)