input: 
  def solve_linear_programming_with_simplex_method(decision_variables, objective_function, constraints):
      """ Explain the approach for solving a linear programming problem using the Simplex method in Python.
      The Simplex Method is an algorithm for solving a linear programming problem, which involves finding 
      the maximum or minimum value of a linear function. This method uses simultaneous equations to define 
      a polytope that contains the solution to the linear programming problem. It then finds the optimal 
      solution in this polytope by iteratively making moves from a vertex to another in the polytope that 
      gives an increase in the message value. 
  
      To solve a linear programming problem using the Simplex Method in Python, first, define the problem 
      in terms of the decision variables, objective function, and constraints. Next, convert the problem 
      into standard form by introducing slack variables, if needed. Then, create a tableau containing 
      the coefficients of the decision variables and the right-hand sides of the constraints, and set up 
      the starting solution in the tableau. Now, iteratively use the Simplex algorithm to move from a 
      vertex to the next until an optimum solution is reached. Determine the optimum solution by inspecting 
      the values in the tableau.
  
      >>> solve_linear_programming_with_simplex_method({'x1', 'x2'}, [3, 2], [[2, 1, 2], [1, 2, 3]])
      'Optimal solution: x1 = 1.0, x2 = 1.0, Objective function value = 7.0'
      """
  
output:     from scipy.optimize import linprog
  
      # Convert the objective function to minimize form
      c = [-coeff for coeff in objective_function]
  
      # Convert constraints to standard form
      A = [constraint[:-1] for constraint in constraints]
      b = [constraint[-1] for constraint in constraints]
  
      # Solve the linear programming problem using the Simplex method
      result = linprog(c, A_ub=A, b_ub=b, method='simplex')
  
      if result.success:
          # Extract the optimal values of the decision variables
          optimal_values = {var: result.x[i] for i, var in enumerate(decision_variables)}
          # Calculate the objective function value
          objective_value = -result.fun
          # Format the result as a string
          solution_str = ', '.join([f'{var} = {val:.1f}' for var, val in optimal_values.items()])
          return f'Optimal solution: {solution_str}, Objective function value = {objective_value:.1f}'
      else:
          return 'No optimal solution found'