What is the difference between dynamic programming and greedy approach

Question

What is the main difference between dynamic programming and greedy approach in terms of usage   As far as I understood  the greedy approach sometimes gives an optimal solution  in other cases  the dynamic programming approach gives an optimal solution    Are there any particular conditions which must be met in order to use one approach  or the other  to obtain an optimal solution

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With the reference of Biswajit Roy   Dynamic Programming firstly plans then Go  and Greedy algorithm uses greedy choice  it firstly Go then continuously Plans

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the major difference between greedy method and dynamic programming is in greedy method only one optimal decision sequence is ever generated and in dynamic programming more than one optimal decision sequence may be generated

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Based on Wikipedia s articles  Greedy Approach A greedy algorithm is an algorithm that follows the problem solving heuristic of making the locally optimal choice at each stage with the hope of finding a global optimum  In many problems  a greedy strategy does not in general produce an optimal solution  but nonetheless a greedy heuristic may yield locally optimal solutions that approximate a global optimal solution in a reasonable time  We can make whatever choice seems best at the moment and then solve the subproblems that arise later  The choice made by a greedy algorithm may depend on choices made so far but not on future choices or all the solutions to the subproblem  It iteratively makes one greedy choice after another  reducing each given problem into a smaller one  Dynamic programming The idea behind dynamic programming is quite simple  In general  to solve a given problem  we need to solve different parts of the problem  subproblems   then combine the solutions of the subproblems to reach an overall solution  Often when using a more naive method  many of the subproblems are generated and solved many times  The dynamic programming approach seeks to solve each subproblem only once  thus reducing the number of computations  once the solution to a given subproblem has been computed  it is stored or  quot memo-ized quot   the next time the same solution is needed  it is simply looked up  This approach is especially useful when the number of repeating subproblems grows exponentially as a function of the size of the input  Difference Greedy choice property We can make whatever choice seems best at the moment and then solve the subproblems that arise later  The choice made by a greedy algorithm may depend on choices made so far but not on future choices or all the solutions to the subproblem  It iteratively makes one greedy choice after another  reducing each given problem into a smaller one  In other words  a greedy algorithm never reconsiders its choices  This is the main difference from dynamic programming  which is exhaustive and is guaranteed to find the solution  After every stage  dynamic programming makes decisions based on all the decisions made in the previous stage  and may reconsider the previous stage s algorithmic path to solution  For example  let s say that you have to get from point A to point B as fast as possible  in a given city  during rush hour  A dynamic programming algorithm will look into the entire traffic report  looking into all possible combinations of roads you might take  and will only then tell you which way is the fastest  Of course  you might have to wait for a while until the algorithm finishes  and only then can you start driving  The path you will take will be the fastest one  assuming that nothing changed in the external environment   On the other hand  a greedy algorithm will start you driving immediately and will pick the road that looks the fastest at every intersection  As you can imagine  this strategy might not lead to the fastest arrival time  since you might take some  quot easy quot  streets and then find yourself hopelessly stuck in a traffic jam  Some other details    In mathematical optimization  greedy algorithms solve combinatorial problems having the properties of matroids  Dynamic programming is applicable to problems exhibiting the properties of overlapping subproblems and optimal substructure

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I would like to cite a paragraph which describes the major difference between greedy algorithms and dynamic programming algorithms stated in the book Introduction to Algorithms  3rd edition  by Cormen  Chapter 15 3  page 381      One major difference between greedy algorithms and dynamic programming is that instead of first finding optimal solutions to subproblems and then making an informed choice  greedy algorithms first make a greedy choice  the choice that looks best at the time  and then solve a resulting subproblem  without bothering to solve all possible related smaller subproblems

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Difference between greedy method and dynamic programming are given below     Greedy method never reconsiders its choices whereas Dynamic programming may consider the previous state    Greedy algorithm is less efficient whereas Dynamic programming is more efficient      Greedy algorithm have a local choice of the sub-problems whereas Dynamic programming would solve the all sub-problems and then select one that would lead to an optimal solution      Greedy algorithm take decision in one time whereas Dynamic programming take decision at every stage

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In simple words we can say that in Dynamic Programming  having problem sending message on network  one can first examine the path which takes the shortest time and then start journey    On the other hand Greedy algorithm take the optimal decision on the spot without thinking for the next step and on the next step change its decision again and so on     Notes  Dynamic programming is reliable while Greedy Algorithms is not reliable always

[algorithm] What is the difference between dynamic programming and greedy approach?

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