What does space complexity in an algorithm refer to?

Space complexity in an algorithm refers to the amount of memory an algorithm requires during its execution. This includes both the memory necessary for the algorithm's variables, intermediate calculations, and any data structures utilized, as well as any additional space required for input data.

Understanding space complexity is crucial for evaluating the efficiency of an algorithm, especially in environments where memory is limited or when processing large datasets. It helps identify how efficiently an algorithm utilizes the available memory resources relative to the size of its input.

The concept of time complexity focuses on the number of operations or the execution time of an algorithm, which is distinct from space complexity that is centered on memory usage. Additionally, while the maximum input size an algorithm can handle is relevant, it is not directly defined by space complexity itself but rather by the requirements of the algorithm and the available resources.