input: Write a function to solve a linear programming problem using the Simplex method in Python. The function should take the decision variables, objective function, and constraints as input, convert the problem into standard form, create a tableau, and iteratively apply the Simplex algorithm to find the optimal solution. If an optimal solution is found, return the values of the decision variables and the optimal value of the objective function. Otherwise, return an indication that no optimal solution exists.
  
  assert simplex_method([3, 4, -2], [[2, 3, 1], [1, -1, 4]], [5, 10])==(3.33, -1.67, 0, 4.33)
  assert simplex_method([-4, 5, 3], [[-1, 2, 1], [1, 4, 2]], [6, 8])==(4, 0, 2, 4)
  assert simplex_method([1, 1, 1], [[1, 1, 1], [1, -1, 0], [0, -1, 1]], [3, 0, 0])==(3, 0, 0, 3)
  assert simplex_method([0, 0, 0], [[1, 2, 3], [2, 3, 4]], [5, 6])==None
  assert simplex_method([1, 1, 1], [], [])==None
output: 
  def simplex_method(c, A, b):
      from scipy.optimize import linprog
      result = linprog(c, A_ub=A, b_ub=b, method='simplex')
      if result.success:
          return tuple(result.x) + (result.fun,)
      else:
          return None
  