Memoization stores the results of expensive function calls to avoid redundant calculations. It’s commonly used in recursive algorithms, especially dynamic programming.

Memoization is a powerful optimization technique in competitive programming that involves storing the results of expensive or repetitive function calls to avoid redundant calculations. It is particularly useful in recursive algorithms, especially in dynamic programming, where many subproblems are solved repeatedly. Memoization can significantly reduce the time complexity of an algorithm by ensuring that each subproblem is only solved once. For example, in the classic Fibonacci sequence problem, a naïve recursive approach has an exponential time complexity because the same subproblems are solved multiple times. By using memoization, you can store the results of each Fibonacci number after it is calculated and reuse them whenever needed, reducing the time complexity to linear. Memoization is typically implemented using a hash table or an array to store the results of subproblems. When a function is called, the algorithm first checks if the result for that input is already stored in the memo. If it is, the stored result is returned immediately; otherwise, the function computes the result and stores it for future use. Memoization is a key tool in solving problems with overlapping subproblems, such as dynamic programming problems like the knapsack problem, longest common subsequence, and many others.