How do dynamic programming and greedy algorithms differ?

How do dynamic programming and greedy algorithms differ?

Dynamic programming solves problems by breaking them down into simpler subproblems and storing their solutions. Greedy algorithms, on the other hand, make the locally optimal choice at each step with the hope of finding a global optimum.

Can you give an example of each?

Sure! An example of dynamic programming is the Fibonacci sequence calculation, where you store previously computed values. A greedy algorithm example is the coin change problem, where you always take the largest denomination first.

When should I use dynamic programming instead of greedy algorithms?

Use dynamic programming when the problem has overlapping subproblems and optimal substructure. Greedy algorithms are suitable when local optimal choices lead to a global optimum.

Are there any downsides to using dynamic programming?

Yes, dynamic programming can be more time-consuming and require more memory due to storing intermediate results, making it less efficient for certain problems.