Space Complexity

Space complexity refers to the amount of memory space required by an algorithm or a program to execute as a function of the input size. It is a measure of how efficiently an algorithm utilizes memory resources.

Similar to time complexity, space complexity is expressed using Big O notation. It provides an upper bound on the amount of memory space used by an algorithm relative to the input size.

Factors

Space complexity considers various factors that contribute to memory usage, including:

• The space required to store input data.
• The space required to store variables, data structures, and other internal data used by the algorithm.
• The space required for recursive function calls and call stacks (in the case of recursive algorithms).
• The space required for auxiliary data structures used by the algorithm, such as arrays, stacks, queues, hash tables, etc.

Like time complexity, understanding space complexity is crucial for designing efficient algorithms and programs, especially in resource-constrained environments such as embedded systems or applications with large datasets. It helps developers make informed decisions about memory usage, optimize performance, and avoid memory-related issues such as memory leaks or out-of-memory errors.

Just like runtime complexity, we use space complexity to help determine which algorithms we want to use.