input: from typing import List
  
  
  def solve_linear_programming_simplex(decision_variables: List[float], objective_function: List[float], constraints: List[List[float]], rhs_constraints: List[float]) -> List[float]:
      """Solve a linear programming problem using the Simplex Method in Python.
      The method involves finding the maximum or minimum value of a linear function
      by iteratively moving from vertex to vertex in a polytope defined by simultaneous
      equations.
      
      Example:
      >>> solve_linear_programming_simplex([1.0, 1.0], [3.0, 4.0], [[1.0, 0.0], [0.0, 1.0]], [5.0, 6.0])
      [5.0, 6.0]
      >>> solve_linear_programming_simplex([1.0, 1.0], [1.0, 2.0], [[1.0, -1.0], [-1.0, 1.0]], [0.0, 0.0]))
      [0.0, 0.0]
      """
output:     import numpy as np
  
      def simplex(A, b, c):
          m, n = A.shape
          tableau = np.zeros((m + 1, n + m + 1))
          tableau[0, :n] = c
          tableau[1:, :n] = A
          tableau[1:, n:n + m] = np.eye(m)
          tableau[1:, -1] = b
  
          while np.any(tableau[0, :-1] > 0):
              pivot_col = np.argmax(tableau[0, :-1])
              ratios = tableau[1:, -1] / tableau[1:, pivot_col]
              ratios[ratios <= 0] = np.inf
              pivot_row = np.argmin(ratios) + 1
  
              tableau[pivot_row] /= tableau[pivot_row, pivot_col]
              for i in range(m + 1):
                  if i != pivot_row:
                      tableau[i] -= tableau[i, pivot_col] * tableau[pivot_row]
  
          return tableau[0, -1] - np.sum(tableau[0, n:n + m] * tableau[1:, -1])
  
      A = np.array(constraints)
      b = np.array(rhs_constraints)
      c = np.array(objective_function)
  
      result = simplex(A, b, c)
      return [result] * len(decision_variables)