input: from typing import List
  
  def solve_linear_programming_simplex(coefficients: List[float], constraints: List[List[float]], rhs: List[float], objective: str) -> List[float]:
      """
      Solve a linear programming problem using the Simplex Method in Python. This function takes the coefficients of the decision variables, the constraints matrix, the right-hand side values, and the objective (either 'max' or 'min').
      The function returns the optimal values of the decision variables.
      
      Example:
      >>> solve_linear_programming_simplex([3, 2], [[1, 0], [0, 1], [1, 1]], [4, 6, 8], 'max')
      [4.0, 4.0]
      """
output:     from scipy.optimize import linprog
  
      num_vars = len(coefficients)
      num_constraints = len(constraints)
  
      # Convert objective to minimization problem
      if objective == 'max':
          coefficients = [-x for x in coefficients]
  
      # Convert constraints to standard form
      constraint_matrix = constraints
      rhs_vector = rhs
  
      # Solve the linear programming problem using scipy's linprog
      result = linprog(coefficients, A_ub=constraint_matrix, b_ub=rhs_vector, method='simplex')
  
      if result.success:
          return result.x
      else:
          raise ValueError("Linear programming problem could not be solved.")