How do I choose between dynamic programming and greedy algorithms?

How do I choose between dynamic programming and greedy algorithms?

Choosing between dynamic programming and greedy algorithms depends on the problem. Dynamic programming is used for optimization problems where the solution can be broken down into overlapping subproblems. Greedy algorithms work well when local optimal choices lead to a global optimum.

Can you give me an example of a problem suited for each?

Sure! A classic example for dynamic programming is the Fibonacci sequence, where each number is the sum of the two preceding ones. For greedy algorithms, consider the coin change problem, where you want to make change for a certain amount using the least number of coins.

What are the key differences in their approaches?

The key difference is that dynamic programming considers all possible solutions and builds up the optimal solution, while greedy algorithms make the best choice at each step without considering future consequences.

How do I know if a problem can be solved with a greedy approach?

A problem can often be solved with a greedy approach if it exhibits the properties of optimal substructure and greedy choice property. If making a local optimal choice leads to a global optimal solution, then a greedy algorithm may be suitable.