For the various problems in area such as inventory, chemical engineering design , and control theory, Dynamic Programming is the only technique used to solve the problem. As I see it for now I can say that dynamic programming is an extension of divide and conquer paradigm. Greedy algorithm is less efficient whereas Dynamic programming is more efficient. Dynamic programming vs Greedy 1. Dynamic Programming is also used in optimization problems. But I hope this article will shed some extra light and help you to do another step of learning such valuable algorithm paradigms as dynamic programming and divide-and-conquer. Moreover, Dynamic Programming algorithm solves each sub-problem just once and then saves its answer in a table, thereby avoiding the work of re-computing the answer every time. Proving that a greedy algorithm is correct is more of an art than a science. Also, dynamic programming, if implemented correctly, guarantees that we get an optimal solution. More efficient as compared,to dynamic programming: Less efficient as compared to greedy approach Advantages 1. 3.2. A Dynamic algorithm is applicable to problems that exhibit Overlapping subproblems and Optimal substructure properties. 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. It involves a lot of creativity. The reason behind dynamic programming optimality is that it’s an optimization over the backtracking approach which explores all the possible choices. The problem can’t be solved until we find all solutions of sub-problems. This video contains the comparison between Greedy method and Dynamic programming. This video contains the comparison between Greedy method and Dynamic programming. Dynamic Programming solves the sub-problems bottom up. DYNAMIC PROGRAMMING. A greedy algorithm is one which finds optimal solution at each and every stage with the hope of finding global optimum at the end. Greedy solves the sub-problems from top down. 2. Example Dynamic Programming and Divide-and-Conquer Similarities. Greed algorithm : Greedy algorithm is one which finds the feasible solution at every stage with the hope of finding global optimum solution. Greedy method never reconsiders its choices whereas Dynamic programming may consider the previous state. Let us say that we have a machine, and to determine its state at time t, we have certain quantities called state variables. Explanation: A greedy algorithm gives optimal solution for all subproblems, but when these locally optimal solutions are combined it may NOT result into a globally optimal solution. Like divide-and-conquer method, Dynamic Programming solves problems by combining the solutions of subproblems. Hence, a greedy algorithm CANNOT be used to solve all the dynamic programming problems. 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