How do greedy algorithms differ from dynamic programming?

Can you explain the difference between greedy algorithms and dynamic programming?

Sure! Greedy algorithms make the locally optimal choice at each stage, while dynamic programming solves problems by breaking them down into simpler subproblems and storing their solutions.

Can you give me an example of a problem that uses dynamic programming?

Absolutely! The Fibonacci sequence is a classic example. Instead of recalculating Fibonacci numbers, dynamic programming stores previously computed values to optimize performance.

What about a problem that is best solved with a greedy algorithm?

A great example is the coin change problem, where you want to make change for a certain amount using the fewest coins possible. A greedy approach works well if the coin denominations are optimal.

Are there any problems where one method is better than the other?

Yes, some problems are best suited for one method over the other. For instance, the Knapsack problem can be solved using dynamic programming for optimal results, while a greedy approach may not yield the best solution.