If you are learning DSA and algorithms, it is really important for you to know how to calculate the Time complexity for any given algorithm. Lets understand the same with example

### Time Complexity Calculation:

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 ( <type> ) where <type> is the measure. The quicksort algorithm would be described as O ( N * log ( N ) ).

Note:

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.

Comment below in case of any suggestions, improvements or discuss the same.

Its simple and understandable, overall good