# How to calculate time complexity

**Introduction:**

Time complexity is a concept in computer science that measures the efficiency of an algorithm based on the number of operations it performs relative to its input size. Calculating time complexity helps developers optimize their code and choose better algorithms when tackling complex problems. In this article, we will discuss how to calculate time complexity using Big O notation.

**1. Understand the Basics of Big O Notation:**

Big O notation is used to represent the upper bound of an algorithm’s time complexity by describing its growth rate as a function of input size (n). It is written as O(f(n)), where f(n) describes the growth rate. Some common examples include:

**– O(1)**: Constant time complexity

**– O(log n):** Logarithmic time complexity

**– O(n):** Linear time complexity

**– O(n log n)**: Linearithmic time complexity

**– O(n^2):** Quadratic time complexity

**– O(2^n):** Exponential time complexity

**2. Break Down the Algorithm into Basic Operations:**

Analyze the algorithm and identify its basic operations, such as arithmetic operations, comparisons, assignments, and function calls. These are the operations whose count will be used to derive the time complexity function f(n).

**3. Determine the Frequency of Each Operation:**

Count how many times each basic operation is executed in relation to the input size (n). For example, if a loop runs ‘n’ times and there are two basic operations inside the loop, each operation will have a frequency of ‘n’.

**4. Derive an Expression for Time Complexity:**

Add up all the terms representing each basic operation multiplied by their frequency. This expression should describe how many operations are performed as a function of input size n.

**Example:** Suppose we have an algorithm to find duplicates in an array with n elements:

**for i =** 0 to n-1:

**for j** = i+1 to n-1:

**if array[i] ==** array[j]:

**print(array[i],** ” is a duplicate”)

There is a nested loop with ‘n’ iterations in the outer loop and ‘n-1’ iterations in the inner loop, so the total number of comparisons is sum of (n-i) for i=0 to n-1; this results in n(n-1)/2 comparisons, which can be expressed as O(n^2).

**5. Simplify the Expression:**

Remove any constants or lower-order terms from the final expression, as they have a minimal impact on the overall time complexity when compared to higher-order terms. In our example above, O(n^2) is already simplified.

**6. Validate Your Calculation:**

Finally, confirm that your calculated time complexity accurately predicts the algorithm’s performance by plotting or measuring its actual execution time for increasing input sizes.

**Conclusion:**

Calculating time complexity using Big O notation allows developers to make informed decisions about choosing more efficient algorithms and optimizing code. By following these steps and understanding the underlying principles, you can effectively calculate time complexity and improve overall program performance.