Greedy algorithms and dynamic programming (DP) are both strategies for solving optimization problems, but they differ in how they approach finding a solution. A greedy algorithm works by making a series of local, optimal choices at each step, with the hope that these local choices will lead to a globally optimal solution. Greedy algorithms are usually more straightforward and faster but don’t always guarantee the best solution, especially for problems where the local optimal choice doesn’t lead to a global optimum. A classic example of a greedy algorithm is the coin change problem, where the goal is to make change for a given amount using the fewest coins possible. The greedy approach might work with specific coin denominations but fail with others. On the other hand, dynamic programming is a more comprehensive approach that considers the entire problem and breaks it down into overlapping subproblems. DP stores the results of these subproblems to avoid redundant calculations, ensuring an optimal solution. Problems like the knapsack problem, longest common subsequence, and matrix chain multiplication are best solved using dynamic programming, where the greedy approach would fail. While greedy algorithms are faster and simpler, dynamic programming is more versatile and can solve a broader range of problems with guaranteed optimal solutions. Understanding when to use greedy algorithms versus dynamic programming is essential for optimizing problem-solving strategies.