Space complexity in algorithms refers to how much memory space an algorithm requires to solve a problem as a function of input size. It contains not just the memory space allocated to the algorithm variables, but also any additional memory space consumed during execution.
Input Space: This refers to the memory space required to store the input data that the algorithm processes. For example, if you have an array of integers as input, the input space would be the memory needed to store that array.
Auxiliary Space: This refers to any extra memory space used by the algorithm during its execution, apart from the input space. This includes space for variables, temporary data structures (like arrays or stacks used in algorithms), recursion stack space, etc.