Member-only story
Calculate Space and Time Complexity of an Algorithm: A Beginner’s Guide
As developers, understanding the efficiency of an algorithm is crucial. Time and space complexity are tools we use to estimate how well an algorithm scales as input size grows. These metrics help us predict performance and choose the best approach for solving a problem.
In this article, we’ll take a detailed, beginner-friendly look at:
- What time and space complexity mean
- How to calculate time complexity step by step
- How to calculate space complexity step by step
- Common Big-O notations
- Examples of analyzing algorithms
By the end, you’ll have the confidence to evaluate algorithms like a pro.
What Is Time Complexity?
Time complexity measures how the runtime of an algorithm changes as the size of the input grows. It’s expressed using Big-O notation, which represents the upper bound of an algorithm’s growth.
For example:
- An algorithm with “ O(n) ” complexity grows linearly with input size.
- An algorithm with “ O(n²) ”complexity grows quadratically.
