- Solve the n-airports problem using gradient based optimization algorithm.
- Find n-airports.ipynb. ii. A random initial state is given as Figure 1a.
(a) An initial state (b) An optimal state
Figure 1: n-airports problem state
- The objective function is given by
n
f(x1,y1,x2,y2,x3,y3) = XX(xi xc)2 + (yi yc)2
i=1 cCi
where n is the number of the airports and Ci is the set of cities whose closest airport is airport i.
- The goal of the program is determining the locations of airports that minimize theobjective function using gradient based optimization. By updating
(x1,y1,x2,y2,x3,y3) (x1,y1,x2,y2,x3,y3) f(x1,y1,x2,y2,x3,y3) where 0 1 is a constant, find an optimal location of the airports as Figure 1b.
- As shown in Figure 2, plot the objective function at every time of updating thelocations to terminate the algorithm. (The objective values may be different than the example.) vi. Submit your n-airports.ipynb.
Figure 2: Objective as a function of epoch
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