Memoization is a technique used in algorithm design to optimize repetitive computations by caching the results of function calls and returning the cached result when the same input is encountered again. This technique helps to reduce the time complexity of algorithms by avoiding redundant calculations.

  In memoization, the idea is to store the results of expensive function calls and retrieve them when needed instead of recomputing. This is particularly useful when a function with the same input might be called multiple times in a recursive algorithm or when solving dynamic programming problems.

  To implement memoization, a data structure, such as a hash table or an array, is used to store the computed results. When a function is called, its input parameters are used as a key to look up the cache. If the result is present in the cache, it is returned immediately. If the result is not found, the function is called normally, and the result is stored in the cache before returning it.

  By storing and reusing the results, memoization can significantly reduce the time complexity of algorithms. This is because the function is only called once for each unique input, and subsequent calls with the same input can retrieve the cached result in constant time. This technique eliminates redundant calculations and improves the efficiency of the algorithm.

  However, it's important to note that memoization is only effective for functions that are pure, meaning they always produce the same output for the same input and have no side effects. If a function has side effects or its output depends on external factors, memoization may lead to incorrect results.

  Overall, memoization is a powerful technique in algorithm design that can greatly improve the efficiency of computations by caching the results of function calls and reusing them when the same input is encountered again.