3.2 Understanding Space Complexity
When we talk about algorithms, two of the most important factors we consider are time complexity and space complexity. Time complexity refers to the amount of time an algorithm takes to execute, whereas space complexity takes into account the amount of memory an algorithm needs to run from start to finish. This means that space complexity is a measure of the total amount of memory an algorithm or operation requires to operate effectively.
It's important to understand space complexity, especially in situations where memory is limited. When you're working with large data sets or resource-intensive applications, the amount of memory required to run an algorithm can be a significant concern. By understanding the space complexity of an algorithm, you can optimize its performance and ensure that it runs efficiently.
Like time complexity, space complexity can be expressed in terms of Big O notation. This notation provides a way to describe the...