input: from typing import List, Tuple
  
  
  def solve_linear_programming_problem(coefficients: List[float], constraints: List[Tuple[List[float], str, float]]) -> Tuple[float, List[float]]:
      """
      Solve a linear programming problem using the Simplex method. The method involves converting the problem into a linear system, 
      setting up a tableau, and iterating until an optimal solution is found. This function takes as input the coefficients of the objective
      function and a list of constraints in the form of tuples, which include the coefficients of the constraint function, the inequality
      direction ('<=', '>=', '=='), and the right-hand side value of the constraint.
      
      Example:
      solve_linear_programming_problem([3, 2], [( [1, 0], '<=', 4 ), ( [0, 1], '<=', 6 ), ( [2, 3], '<=', 18 )])
      Output:
      (36.0, [4.0, 6.0])  # Optimal value and values for the decision variables.
      """
output:     from scipy.optimize import linprog
  
      # Convert the constraints to the format required by scipy.optimize.linprog
      A = []
      b = []
      for constraint in constraints:
          A.append(constraint[0])
          if constraint[1] == '<=':
              b.append(constraint[2])
          elif constraint[1] == '>=':
              A[-1] = [-x for x in A[-1]]
              b.append(-constraint[2])
          elif constraint[1] == '==':
              A.append(constraint[0])
              b.append(constraint[2])
              A.append([-x for x in constraint[0]])
              b.append(-constraint[2])
  
      # Solve the linear programming problem
      result = linprog(c=-coefficients, A_ub=A, b_ub=b, method='simplex')
  
      # Return the optimal value and the values for the decision variables
      return (-result.fun, result.x)