Which of the following techniques can enhance the efficiency of recursive algorithms?

Memoization is a technique that significantly enhances the efficiency of recursive algorithms by storing the results of expensive function calls and reusing those results when the same inputs occur again. This approach avoids the redundant computations that typically occur in naive recursive solutions, particularly in problems like computing Fibonacci numbers or looking up values in dynamic programming scenarios.

When an algorithm has overlapping subproblems, as many recursive algorithms do, memoization can decrease the time complexity from exponential to polynomial by ensuring that each unique subproblem is solved only once. Once a result is computed, it is cached, so subsequent calls with the same parameters can be resolved in constant time, greatly improving overall performance.

In contrast, the other techniques, while useful in different contexts, do not specifically address the efficiency of recursive algorithms in the same way that memoization does. For instance, backtracking focuses on solving constraint satisfaction problems and usually involves exploring multiple possibilities without necessarily optimizing overlapping calculations. Greedy selection works by making a series of choices that result in an optimal solution at each individual step but does not apply to all recursive situations. Simple iteration, while often effective for looping structures, does not inherently leverage recursion and thus is not a direct enhancement for recursive algorithm efficiency.

Overall, the use of memoization directly targets