When should you avoid using dynamic programming to solve a problem?
- When the problem can be solved using a greedy algorithm.
- When the problem can be solved using recursion efficiently.
- When the problem does not have overlapping subproblems.
- When the problem has a small input size.
Dynamic programming is most effective when a problem exhibits overlapping subproblems, meaning the same subproblems are solved multiple times in the process. If a problem does not have this characteristic, dynamic programming may not offer significant advantages over other approaches like recursion or greedy algorithms. Additionally, for problems with very small input sizes, the overhead of dynamic programming (such as building tables or memoization arrays) might outweigh the benefits, making simpler algorithms more suitable.
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