Final answer:
The greedy method does not guarantee optimality or use recursion, while dynamic programming guarantees both optimality and recursion in its approach.
Step-by-step explanation:
Greedy method and dynamic programming are both problem-solving techniques in computer science.
i) Feasibility:
In terms of feasibility, the greedy method does not guarantee an optimal solution but can often find a feasible solution quickly.
On the other hand, dynamic programming guarantees both feasibility and optimality. It breaks down a problem into smaller subproblems and solves them recursively.
ii) Optimality:
The greedy method does not always produce an optimal solution as it makes locally optimal choices without considering the entire problem space.
Dynamic programming, however, guarantees an optimal solution by considering all possible subproblems and making the best choice at each step.
iii) Recursion:
The greedy method does not usually involve recursion. Instead, it typically uses an iterative approach to make greedy choices.
Dynamic programming, on the other hand, often involves recursion as it breaks down the problem into smaller subproblems and solves them recursively.
iv) Memorization:
The greedy method does not require memorization since it does not revisit previous choices.
Dynamic programming, on the other hand, can employ memorization techniques like memoization or tabulation to store and reuse the results of previously solved subproblems.
v) Time Complexity:
The time complexity of the greedy method depends on the problem, but it is generally faster than dynamic programming.
Dynamic programming has a higher time complexity as it solves overlapping subproblems and can involve recalculating the same subproblem multiple times.