Space Complexity

Space complexity is a crucial measure of an algorithm's efficiency in computer science. It measures how much memory or space is required to execute an algorithm. When we analyze an algorithm, we need to consider not only how fast it works (time complexity) but also how much memory it uses.

Space complexity is the total memory used by an algorithm based on the size of the input. It is usually measured in memory units (like bytes, kilobytes, etc.). It has two main components: Fixed Space (memory that does not depend on the input size) and Variable Space (memory that depends on the input size).

The main purpose of space complexity is to know how much memory is being used by an algorithm, which is especially important when working with large datasets. If an algorithm requires too much memory, it can slow down the system or even cause it to crash.

It includes independent and dependent space, accounts for both static and dynamic memory allocation, indicates the efficiency of memory use, and helps in analyzing performance on large datasets. There is often a time-space tradeoff to consider in algorithm design.