Problems with complex recursive structures often lead to inefficient solutions because of repeated calculations in overlapping subproblems. Memoization, a technique that stores the results of expensive function calls and reuses them when the same inputs occur again, can dramatically speed up these solutions. By converting a recursive solution into a top-down dynamic programming (DP) approach with memoization, you can avoid recalculating subproblems. Alternatively, consider transforming the recursive solution into an iterative bottom-up DP approach, which is usually more space-efficient and avoids the limitations of recursion depth. These techniques ensure that complex recursive problems can be solved efficiently, even with large input sizes.