Big O, How Do You Calculate/Approximate It?

When it comes to analyzing the performance of algorithms, one key concept that every computer science graduate learns is Big O notation. Big O notation, also referred to as time complexity or algorithmic complexity, allows us to measure and compare the efficiency of different algorithms as the input size grows. In simpler terms, it helps us understand how well an algorithm scales.

What is Big O Notation?

In a nutshell, Big O notation is a mathematical notation that describes the upper bound of the growth rate of a function or algorithm. It provides a standard way to classify algorithms based on how their performance scales with the input size.

Big O notation is typically represented using the letter "O" followed by a function, such as O(n), O(n^2), O(log n), etc. The function inside the parentheses represents the relationship between the input size (n) and the number of operations performed by the algorithm.

Let's look at some common Big O notations:

  • O(1) - Constant time complexity
  • O(log n) - Logarithmic time complexity
  • O(n) - Linear time complexity
  • O(n^2) - Quadratic time complexity
  • O(2^n) - Exponential time complexity

Understanding these notations and being able to calculate or approximate the complexity of an algorithm is essential for writing efficient code.

How to Calculate Big O Complexity

Calculating the exact Big O complexity of an algorithm can be quite challenging, especially for complex algorithms. However, there are some general guidelines and techniques that can help us determine the Big O complexity.

1. Counting Operations

One approach to calculate the complexity is to count the number of operations executed by the algorithm as a function of the input size. This can be done by analyzing the code and identifying the number of loops, conditionals, and other repetitive instructions.

Let's take a simple example of finding the maximum number in an array:

        int findMax(int[] arr) {
            int max = arr[0];
            for (int i = 1; i < arr.length; i++) {
                if (arr[i] > max) {
                    max = arr[i];
            return max;

In this example, we have a loop that iterates through the array once. As the input size increases, the number of iterations also increases linearly. Therefore, the complexity of this algorithm can be represented as O(n), where n is the size of the input array.

2. Eliminate Constants

When analyzing an algorithm, it is common to ignore the constant factors and focus on the dominant term. This is because the constant factors become less significant as the input size grows. For example, if an algorithm has a time complexity of 5n^2 + 10n + 3, it can be simplified to O(n^2) by eliminating the constants.

3. Analyzing Loops

Loops are a common factor in algorithms, and they have a significant impact on the complexity. Different types of loops have different complexities:

  • A loop that iterates through n elements has a linear complexity of O(n).
  • A nested loop that iterates through n elements for each element has a quadratic complexity of O(n^2).
  • A loop that divides the input size in half at each iteration has a logarithmic complexity of O(log n).

It is important to carefully analyze the loops in your algorithm to determine their impact on the overall complexity.

4. Space Complexity

In addition to time complexity, algorithms also have space complexity, which measures the amount of memory required by an algorithm. The space complexity is often represented in terms of additional memory used by an algorithm relative to the input size.

For example, let's consider an algorithm that creates a new array of size n:

        int[] createArray(int n) {
            int[] arr = new int[n];
            return arr;

In this case, the space complexity is O(n) because the algorithm requires memory proportional to the input size.

Approximating Big O Complexity

While it is possible to calculate the exact Big O complexity of some algorithms, there are cases where approximation is necessary. This is particularly true for algorithms with complex control flow or recursive calls.

1. Best, Worst, and Average Cases

An algorithm's complexity can vary depending on the input. In some cases, it may have different complexities for different scenarios. It is common to analyze the best-case, worst-case, and average-case scenarios to approximate the complexity.

For example, consider a sorting algorithm such as Bubble Sort. In the worst case, where the input is in reverse order, Bubble Sort has a complexity of O(n^2). However, in the best case, where the input is already sorted, Bubble Sort has a complexity of O(n). By considering these different scenarios, we can approximate the overall complexity.

2. Master Theorem

The Master Theorem is a mathematical formula that can be used to approximate the complexity of divide and conquer algorithms, specifically those that divide the problem into subproblems of equal size.

The formula states that if an algorithm solves a problem of size n by dividing it into a subproblem of size n/b and performing work of size f(n), then the complexity can be approximated as follows:

        T(n) = aT(n/b) + f(n)

By applying the Master Theorem, we can estimate the complexity of algorithms like binary search, merge sort, and quicksort.


Big O notation is a powerful tool that allows us to analyze and compare the efficiency of algorithms. While calculating the exact complexity can be challenging, using techniques like counting operations, analyzing loops, and eliminating constants helps approximate the complexity.

Understanding the concepts and techniques discussed in this article will empower you to write more efficient code and make informed decisions when choosing algorithms for different tasks.