input: 
  def solve_linear_programming_problem(decision_variables, objective_function, constraints):
      """
      Solve a linear programming problem using the Simplex Method. This method involves finding the
      maximum or minimum value of a linear function by defining a polytope that contains the solution
      and then iteratively moving from vertex to vertex within this polytope to find the optimal 
      solution.
      
      Example:
      solve_linear_programming_problem(['x1', 'x2'], [3, 4], [['LE', 2, [1, 0]], ['LE', 5, [0, 1]]])
      """
  
output:     from scipy.optimize import linprog
  
      # Convert objective function to minimization form
      c = [-coeff for coeff in objective_function]
  
      # Convert constraints to standard form
      A = []
      b = []
      for constraint in constraints:
          if constraint[0] == 'LE':
              A.append([-coeff for coeff in constraint[2]])
              b.append(-constraint[1])
          elif constraint[0] == 'GE':
              A.append(constraint[2])
              b.append(constraint[1])
          elif constraint[0] == 'EQ':
              A.append(constraint[2])
              b.append(constraint[1])
              A.append([-coeff for coeff in constraint[2]])
              b.append(-constraint[1])
  
      # Solve the linear programming problem
      result = linprog(c, A_ub=A, b_ub=b, method='simplex')
  
      # Check if the problem was solved successfully
      if result.success:
          solution = {var: result.x[i] for i, var in enumerate(decision_variables)}
          return solution
      else:
          return None