Space and Time Complexity are measures of an algorithm’s efficiency. Space complexity measures how much memory an algorithm uses relative to the input size, including variables, data structures, and function call stacks. Time complexity refers to how the execution time grows as the input size increases. Both are commonly expressed using Big O notation, like O(1), O(n), or O(n²). These complexities help compare algorithms and choose the most efficient one for a problem.

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