Greedy algorithms make local optimal choices at each step, while dynamic programming considers all possible subproblems to find the global optimum.

Greedy algorithms and dynamic programming are two different approaches to solving optimization problems, and they each have their strengths and weaknesses. Greedy algorithms work by making the locally optimal choice at each step with the hope that these local choices will lead to a globally optimal solution. For example, in the problem of finding the minimum number of coins to make a certain amount of change, a greedy algorithm might always choose the largest available coin first. Greedy algorithms are often faster and simpler to implement than dynamic programming solutions, but they don't always guarantee the correct solution for all problems. They are most effective when a problem exhibits the greedy-choice property, meaning that a locally optimal choice leads to a globally optimal solution. Dynamic programming, on the other hand, involves solving subproblems and combining their solutions to solve the overall problem. It works by considering all possible ways to break down the problem and then using the results of these subproblems to build the final solution. Dynamic programming guarantees an optimal solution but is usually more complex and slower than a greedy approach. It is most useful when a problem exhibits overlapping subproblems and an optimal substructure. The key difference between the two approaches is that greedy algorithms make decisions based on local information, while dynamic programming takes a global view by considering all possible subproblems.