input: def simplex_linear_programming(decision_variables, objective_function, constraints):
      """ Solves a linear programming problem using the Simplex method. The Simplex method
      is an algorithm for finding the maximum or minimum value of a linear function.
      It uses simultaneous equations to define a polytope that contains the solution, and 
      iteratively moves from a vertex to another in the polytope to reach the optimal solution.
      
      Example:
      >>> simplex_linear_programming([2, 3], [4, 5], [[1, 2, 10], [3, 2, 15]])
      [3.0, 1.0, 4.0]
      """
output:     from scipy.optimize import linprog
  
      # Convert the objective function to a minimization problem
      c = [-x for x in objective_function]
  
      # Extract the coefficients and constants from the constraints
      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, bounds=[(0, None)] * len(decision_variables))
  
      # Return the optimal values of the decision variables
      return result.x