input: def solve_linear_programming_simplex_method(objective_function: str, constraint_matrix: List[List[float]], constraint_values: List[float], decision_variables: List[str], maximize: bool) -> List[float]:
      """
      Solve a linear programming problem using the Simplex Method in Python.
      The Simplex Method is an algorithm for solving a linear programming problem, which involves finding the maximum or minimum value of a linear function. This method uses simultaneous equations to define a polytope that contains the solution to the linear programming problem. It then finds the optimal solution in this polytope by iteratively making moves from a vertex to another in the polytope that gives an increase in the message value.
  
      To solve a linear programming problem using the Simplex Method in Python, first, define the problem in terms of the decision variables, objective function, and constraints. Next, convert the problem into standard form by introducing slack variables, if needed. Then, create a tableau containing the coefficients of the decision variables and the right-hand sides of the constraints, and set up the starting solution in the tableau. Now, iteratively use the Simplex algorithm to move from a vertex to the next until an optimum solution is reached. Determine the optimum solution by inspecting the values in the tableau.
  
      Example:
      
      objective_function = "3x + 4y"
      constraint_matrix = [[1, 1], [2, 1]]
      constraint_values = [4, 5]
      decision_variables = ["x", "y"]
      maximize = True
      solve_linear_programming_simplex_method(objective_function, constraint_matrix, constraint_values, decision_variables, maximize)
      
      Output:
      
      [2.0, 2.0]
      
      """
output:     from scipy.optimize import linprog
  
      # Convert objective function to a list of coefficients
      objective_coefficients = [float(coef) for coef in objective_function.split() if coef.replace('.', '', 1).isdigit()]
      if not maximize:
          objective_coefficients = [-coef for coef in objective_coefficients]
  
      # Solve the linear programming problem using scipy's linprog
      result = linprog(c=objective_coefficients, A_ub=constraint_matrix, b_ub=constraint_values, method='simplex')
  
      # Extract the solution values
      solution = result.x.tolist()
  
      return solution