There is a small company which in some months maintains an office in Albany (code A) and in others in Beaverton (code B), and moves back and forth between these two cities (they can only afford to have one office 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 M, a fixed cost of moving between the two cities, and lists A = (a1, . . . , an) and B = (b1, . . . , bn). Here ai is the cost of operating out of Albany in month i, and bi is the cost of being in Beaverton that month. Suppose that M = 10, A = (1, 3, 20, 30), and B = (50, 20, 2, 4). If the location plan is (A, A, B, B), its cost will be 1 + 3 + 10 + 2 + 4 = 20. On the other hand, the cost of the plan (B, B, A, B) is 50 + 20 + 10 + 20 + 10 + 4 = 114. The goal here is to (start to) devise a dynamic programming algorithm which, given M, A, and B, 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.