Introduction:
Dynamic Programming is a problem-solving technique that involves breaking down a complex problem into smaller overlapping subproblems and solving them individually. It is typically used when the problem exhibits optimal substructure and overlapping subproblems. However, a greedy approach is not a required characteristic for a problem to be solved using Dynamic Programming.

Explanation:
- Overlapping subproblems: One of the key characteristics of a problem that can be solved using Dynamic Programming is overlapping subproblems. This means that the problem can be divided into smaller subproblems, and the solution to the larger problem can be constructed from the solutions of the smaller subproblems. These subproblems should be overlapping, meaning that the same subproblems are solved multiple times.

- Optimal substructure: Another required characteristic for a problem to be solved using Dynamic Programming is optimal substructure. This means that the optimal solution to the problem can be constructed from the optimal solutions of its subproblems. In other words, the problem must have a recursive structure where the optimal solution can be expressed in terms of optimal solutions to smaller subproblems.

- Recursion or recursion-like structure: Dynamic Programming often involves solving the subproblems recursively or using a recursion-like structure. This allows the solutions to the smaller subproblems to be reused and built upon to solve the larger problem. Recursion or recursion-like structure is a common feature of problems that can be solved using Dynamic Programming.

- Greedy approach: A greedy approach is not a required characteristic for a problem to be solved using Dynamic Programming. Greedy algorithms make locally optimal choices at each step, hoping that these choices will lead to a globally optimal solution. While some problems can be solved using both Dynamic Programming and a greedy approach, they are not interchangeable. Dynamic Programming involves solving subproblems and building up to the optimal solution, while a greedy approach focuses on making locally optimal choices without considering the overall structure of the problem.

Conclusion:
In conclusion, a greedy approach is not a required characteristic for a problem to be solved using Dynamic Programming. The required characteristics include overlapping subproblems, optimal substructure, and recursion or recursion-like structure. By leveraging these characteristics, Dynamic Programming allows for efficient and effective problem-solving.