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A recursive approach is less efficient than iteration when it leads to a higher time complexity or excessive memory usage.

Recursion is a powerful tool in computer science, allowing us to solve complex problems by breaking them down into simpler, smaller problems. However, it's not always the most efficient approach. In some cases, using recursion can lead to a higher time complexity, meaning the algorithm takes longer to run. This is particularly true for problems where the same sub-problem is solved multiple times, as in the case of calculating Fibonacci numbers. An iterative approach, on the other hand, can solve these problems more efficiently by avoiding this repetition.

Another issue with recursion is that it can lead to excessive memory usage. Each recursive call adds a new layer to the call stack, which requires additional memory. If the recursion is too deep, this can lead to a stack overflow error. Iterative solutions, in contrast, typically use a constant amount of memory, making them more efficient for problems with large inputs.

Furthermore, recursion can be less efficient due to the overhead associated with function calls. Each recursive call involves pushing and popping operations on the system stack, which can be time-consuming. Iterative solutions avoid this overhead by using loops, which can be faster.

However, it's important to note that the efficiency of recursion versus iteration can depend on the specific problem and the specific implementation. In some cases, a well-designed recursive solution can be just as efficient, if not more so, than an iterative one. It's also worth noting that some problems are naturally suited to a recursive approach and can be much more difficult to solve iteratively. Therefore, while recursion can be less efficient in some cases, it's still a valuable tool in a programmer's toolkit.

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