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This video contains the comparison between Greedy method and Dynamic programming. DYNAMIC PROGRAMMING. More efficient as compared,to dynamic programming: Less efficient as compared to greedy approach 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. Greedy method never reconsiders its choices whereas Dynamic programming may consider the previous state. 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. Dynamic programming vs Greedy 1. The problem can’t be solved until we find all solutions of sub-problems. Advantages 1. The reason behind dynamic programming optimality is that it’s an optimization over the backtracking approach which explores all the possible choices. Proving that a greedy algorithm is correct is more of an art than a science. Greedy solves the sub-problems from top down. Also, dynamic programming, if implemented correctly, guarantees that we get an optimal solution. Dynamic Programming is also used in optimization problems. 2. Like divide-and-conquer method, Dynamic Programming solves problems by combining the solutions of subproblems. Dynamic Programming and Divide-and-Conquer Similarities. This video contains the comparison between Greedy method and 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. 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. Let us say that we have a machine, and to determine its state at time t, we have certain quantities called state variables. 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. Example It involves a lot of creativity. 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. A Dynamic algorithm is applicable to problems that exhibit Overlapping subproblems and Optimal substructure properties. Greed algorithm : Greedy algorithm is one which finds the feasible solution at every stage with the hope of finding global optimum solution. Dynamic Programming solves the sub-problems bottom up. 3.2. Devise the greedy algorithm that makes the change of n rupees (n<55000 and n is multiple of 10) with minimum number of notes (consider 100 notes of 10 rupees, 80 notes 20 rupees, 60 notes of 50 rupees, 50 notes of 100 rupees, 40 notes of 500 rupees and 30 notes of 1000 rupees). 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