TSP問題を動的計画法で解くソースコードを示せ。変数dfにはx座標とy座標のデータが入っており、pulpを使用する。

def tsp(df):
  N = len(df)
  d = [[0]*N for i in range(N)]
  for i in range(N):
    for j in range(N):
      if i == j:
        continue
      d[i][j] = np.linalg.norm(df[i]-df[j])
  m = pulp.LpProblem('tsp', pulp.LpMinimize)
  x = [[pulp.LpVariable('x({},{})'.format(i,j), cat='Binary') for j in range(N)] for i in range(N)]
  m += pulp.lpSum(d[i][j]*x[i][j] for i in range(N) for j in range(N))
  for i in range(N):
    m += pulp.lpSum(x[i][j] for j in range(N)) == 1
    m += pulp.lpSum(x[j][i] for j in range(N)) == 1
  m += pulp.lpSum(x[