input: from typing import List
  
  def solve_linear_programming_problem(objective_function: List[float], constraints: List[List[float]], RHS: List[float]) -> List[float]:
      """ Solve a linear programming problem using the Simplex Method. The objective is to 
      either maximize or minimize the given linear function. Convert the problem into
      standard form, introduce slack variables if necessary, and iteratively move from
      one vertex to another in the polytope until an optimal solution is reached.
      
      >>> solve_linear_programming_problem([1, 2], [[-1, 1], [2, 1]], [1, 3])
      [2.5, 0.0]
      >>> solve_linear_programming_problem([1, 1], [[1, 1], [1, 0], [0, 1]], [6, 4, 4])
      [4.0, 2.0]
      """
output:     from scipy.optimize import linprog
  
      # Convert the objective function to a minimization problem
      c = [-x for x in objective_function]
  
      # Convert constraints to the form required by linprog
      A = constraints
      b = RHS
  
      # Solve the linear programming problem
      result = linprog(c, A_ub=A, b_ub=b, method='simplex')
  
      # Return the optimal solution
      return result.x