How does dynamic programming compare to greedy algorithms?

What is the main difference between dynamic programming and greedy algorithms?

Dynamic programming solves problems by breaking them down into simpler subproblems and storing their solutions, while greedy algorithms make the locally optimal choice at each step, hoping to find a global optimum.

Can you give an example of a problem where dynamic programming is better than a greedy approach?

Sure! The Knapsack problem is a classic example where dynamic programming is more effective, as it considers all possible combinations of items, while a greedy approach might miss the optimal solution.

Are there any problems where greedy algorithms work better?

Yes, the Activity Selection problem is a good example where a greedy algorithm works best, as it selects the next activity that finishes the earliest.

How do I decide which approach to use for a given problem?

You should analyze the problem's structure. If it has overlapping subproblems and optimal substructure, dynamic programming is likely the way to go. If it can be solved by making a series of local optimal choices, a greedy algorithm may be more suitable.