Explain the concept of memoization and where it is typically used.

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Memoization is a technique where the results of expensive function calls are saved the first time they’re computed so that subsequent calls with the same inputs can reuse the cached value instead of recomputing. This is especially powerful in dynamic programming and recursion-heavy algorithms because subproblems are solved repeatedly, and redoing those computations causes exponential growth. In practice you store results in a cache (like a map) keyed by the function’s arguments; when the function runs, you check the cache first, returning the cached result if present, or compute and then store it if not. A classic example is computing Fibonacci numbers with naive recursion, where memoization reduces the work from exponential to linear time by avoiding recomputation of already solved subproblems. The other ideas don’t fit because recomputing every time is the opposite of memoization, replacing calls with loops isn’t about caching results, and storing only the final result ignores intermediate subproblem results that memoization preserves.