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There is a small real estate firm which in some months maintains an office in Coquille, OR (code C) and in others in Drain, OR

  1. There is a small real estate firm which in some months maintains an office in Coquille, OR (code C) and in others in Drain, OR (code D), and moves back and forth between these two cities (they can only afford to have one location operating at a time). This company wants to have the cheapest possible location plan - the two cities have different operating costs and these costs can change from month to month. We are given image text in transcribed, a fixed cost of moving between the two cities, and lists image text in transcribed and image text in transcribed. Here image text in transcribed is the cost of operating out of Coquille in month image text in transcribed and is the cost of being in Drain that month. Suppose that image text in transcribed, image text in transcribed and image text in transcribed . If the location plan is image text in transcribed, its cost will be image text in transcribed . On the other hand, the cost of the plan image text in transcribed is image text in transcribed . The goal here is to (start to) devise a dynamic programming algorithm which, given image text in transcribed and image text in transcribed determines the cost of the optimal plan. The plan can start in either city, and end in either city. Note that you will likely need two subproblems, which will be mutually recursive.
C = (C1, ...) D = (d1, ...dn) M = 10 C = (1,3, 20, 30) D = (50,20,2, 4) (C,C,D,D 1+3+ 10 +2+4= 20 (D, D, C, D) 50+ 20+10+20+10+4=114 M, C, C = (C1, ...) D = (d1, ...dn) M = 10 C = (1,3, 20, 30) D = (50,20,2, 4) (C,C,D,D 1+3+ 10 +2+4= 20 (D, D, C, D) 50+ 20+10+20+10+4=114 M, C

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