What does “space complexity” refer to?

Space complexity refers to the amount of working storage—both temporary and permanent—that an algorithm requires to execute. This includes all types of memory allocation: the space needed for variables, data structures, function call stacks, and any additional memory that may be used during execution. By assessing space complexity, you can gauge how efficiently an algorithm utilizes memory resources, which is crucial for optimizing performance, especially in environments with limited memory capacity.

In contrast, the total amount of input data pertains more to the problem size rather than the algorithm’s needs. The number of steps an algorithm takes to complete is related to time complexity, focusing on the execution time rather than memory usage. Lastly, the maximum number of nodes in a data structure may reflect certain characteristics of the structure itself, but it does not provide a comprehensive measure of the memory requirements of an algorithm. Thus, focusing on the working storage is key to understanding space complexity.