Which method is often used to optimize recursive algorithms?

Memoization is a powerful technique used to optimize recursive algorithms by storing the results of expensive function calls and reusing them when the same inputs occur again. This approach is particularly beneficial for problems that exhibit overlapping subproblems and optimal substructure properties, such as in dynamic programming.

When a recursive algorithm is implemented naively, it may perform redundant calculations for the same inputs, leading to inefficient performance characterized by exponential time complexity. By integrating memoization, the algorithm saves previously computed results in a structured form (often using a hash table or an array), which allows it to retrieve the results in constant time during subsequent calls. As a result, the overall time complexity of the algorithm can often be reduced to polynomial time, making it significantly faster.

In contrast, while dynamic programming refers to a broader strategy that encompasses both memoization and tabulation, it does not exclusively define the optimization of recursive algorithms. Greedy algorithms provide a different problem-solving approach that works best for optimization problems where local choices lead to a global optimum, but they do not directly optimize recursive algorithms. The iterative approach, while a valid way to implement algorithms, is distinct from the optimization of recursion specifically and may not always address the underlying inefficiencies of a recursive implementation.