Memoization is an optimization technique where the results of expensive function calls are stored so they don’t have to be recalculated. It’s commonly used in dynamic programming.

Memoization is an optimization technique used to speed up function execution by caching the results of expensive function calls. When a function is called with a specific set of arguments, memoization stores the result so that if the function is called again with the same arguments, the stored result is returned instead of recalculating the value. Memoization is particularly useful in recursive algorithms, where the same subproblem may be solved multiple times. By storing the result of each subproblem, memoization reduces the time complexity of the algorithm from exponential to polynomial. In TypeScript, memoization can be implemented using objects or the Map class to store key-value pairs, where the function arguments serve as the key and the computed result is the value. A common example of memoization is in solving the Fibonacci sequence, where the naive recursive solution recalculates the same Fibonacci numbers multiple times. With memoization, each Fibonacci number is computed only once, reducing the time complexity from O(2^n) to O(n). Memoization is widely used in dynamic programming, particularly in problems with overlapping subproblems and optimal substructure. Understanding memoization is essential for writing efficient recursive algorithms and solving complex problems faster.