Step 1: Understand what space complexity means. Space complexity measures how much extra memory an algorithm uses as the input size grows.
Step 2: Know that binary search is an algorithm used to find an item in a sorted list by repeatedly dividing the search interval in half.
Step 3: Recognize that binary search can be implemented in two ways: iteratively (using loops) and recursively (using function calls).
Step 4: Focus on the iterative implementation of binary search. In this method, you only need a few variables to keep track of the current position and the search boundaries.
Step 5: Since the iterative version uses a constant amount of extra space (regardless of the size of the input list), its space complexity is O(1).
Step 6: If binary search is implemented recursively, it would use additional space for each function call, leading to a space complexity of O(log n), but we are focusing on the iterative version.
