Solutions to problems using dynamic programming or greedy methods?

What properties should the problem have so that I can decide which method to use dyn开发者_开发问答amic programming or greedy method?

Dynamic programming problems exhibit optimal substructure. This means that the solution to the problem can be expressed as a function of solutions to subproblems that are strictly smaller.

One example of such a problem is matrix chain multiplication.

Greedy algorithms can be used only when a locally optimal choice leads to a totally optimal solution. This can be harder to see right away, but generally easier to implement because you only have one thing to consider (the greedy choice) instead of multiple (the solutions to all smaller subproblems).

One famous greedy algorithm is Kruskal's algorithm for finding a minimum spanning tree.

The second edition of Cormen, Leiserson, Rivest and Stein's Algorithms book has a section (16.4) titled "Theoretical foundations for greedy methods" that discusses when the greedy methods yields an optimum solution. It covers many cases of practical interest, but not all greedy algorithms that yield optimum results can be understood in terms of this theory.

I also came across a paper titled "From Dynamic Programming To Greedy Algorithms" linked here that talks about certain greedy algorithms can be seen as refinements of dynamic programming. From a quick scan, it may be of interest to you.

There's really strict rule to know it. As someone already said, there are some things that should turn the red light on, but at the end, only experience will be able to tell you.

We apply greedy method when a decision can be made on the local information available at each stage.We are sure that following the set of decisions at each stage,we will find the optimal solution. However, in dynamic approach we may not be sure about making a decision at one stage, so we carry a set of probable decisions , one of the probable elements may take to a solution.