input: Write a function to explain the approach for solving a linear programming problem using the Simplex method in Python. If the input is a linear programming problem, then return a detailed explanation of how to solve the problem using the Simplex method, including the steps to convert the problem into standard form, set up the tableau, and iteratively use the Simplex algorithm to find the optimal solution.
  assert solve_lp_using_simplex("maximize 3x + 4y subject to x + y <= 5, x >= 0, y >= 0")==\
  "To solve the given linear programming problem using the Simplex Method in Python: \
  1. Define the problem: maximize 3x + 4y subject to x + y <= 5, x >= 0, y >= 0. \
  2. Convert the problem into standard form by introducing slack variables: maximize 3x + 4y + 0s1 + 0s2, subject to x + y + s1 = 5, x >= 0, y >= 0, s1 >= 0, s2 >= 0. \
  3. Create a tableau with coefficients and constraints. \
  4. Iteratively use the Simplex algorithm to move from a vertex to the next until an optimum solution is reached. \
  5. Determine the optimum solution by inspecting the values in the tableau."
  assert solve_lp_using_simplex("minimize 2x - 3y subject to 2x + y >= 4, x - y = 1, x >= 0, y >= 0")==\
  "To solve the given linear programming problem using the Simplex Method in Python: \
  1. Define the problem: minimize 2x - 3y subject to 2x + y >= 4, x - y = 1, x >= 0, y >= 0. \
  2. Convert the problem into standard form by introducing slack variables: minimize 2x - 3y + 0s1 + 0s2, subject to 2x + y - s1 = 4, x - y = 1, x >= 0, y >= 0, s1 >= 0, s2 >= 0. \
  3. Create a tableau with coefficients and constraints. \
  4. Iteratively use the Simplex algorithm to move from a vertex to the next until an optimum solution is reached. \
  5. Determine the optimum solution by inspecting the values in the tableau."
  assert solve_lp_using_simplex("maximize 5x1 + 6x2 subject to x1 + x2 <= 3, x1 >= 1, x2 >= 1")==\
  "To solve the given linear programming problem using the Simplex Method in Python: \
  1. Define the problem: maximize 5x1 + 6x2 subject to x1 + x2 <= 3, x1 >= 1, x2 >= 1. \
  2. Convert the problem into standard form by introducing slack variables: maximize 5x1 + 6x2 + 0s1 + 0s2, subject to x1 + x2 + s1 = 3, x1 >= 1, x2 >= 1, s1 >= 0, s2 >= 0. \
  3. Create a tableau with coefficients and constraints. \
  4. Iteratively use the Simplex algorithm to move from a vertex to the next until an optimum solution is reached. \
  5. Determine the optimum solution by inspecting the values in the tableau."
  assert solve_lp_using_simplex("minimize x + y subject to x + 2y >= 4, 2x + y >= 4, x >= 0, y >= 0")==\
  "To solve the given linear programming problem using the Simplex Method in Python: \
  1. Define the problem: minimize x + y subject to x + 2y >= 4, 2x + y >= 4, x >= 0, y >= 0. \
  2. Convert the problem into standard form by introducing slack variables: minimize x + y + 0s1 + 0s2, subject to x + 2y - s1 = 4, 2x + y - s2 = 4, x >= 0, y >= 0, s1 >= 0, s2 >= 0. \
  3. Create a tableau with coefficients and constraints. \
  4. Iteratively use the Simplex algorithm to move from a vertex to the next until an optimum solution is reached. \
  5. Determine the optimum solution by inspecting the values in the tableau."
  assert solve_lp_using_simplex("")==\
  "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."
output: def solve_lp_using_simplex(problem):
      if not problem:
          return "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."
      
      parts = problem.split("subject to")
      objective = parts[0].strip()
      constraints = parts[1].strip().split(", ")
      
      if "maximize" in objective:
          objective_type = "maximize"
          objective_func = objective.replace("maximize ", "").strip()
      else:
          objective_type = "minimize"
          objective_func = objective.replace("minimize ", "").strip()
      
      explanation = f"To solve the given linear programming problem using the Simplex Method in Python: \n1. Define the problem: {objective} subject to {', '.join(constraints)}. \n2. Convert the problem into standard form by introducing slack variables: {objective_type} {objective_func} + 0s1 + 0s2, subject to "
      
      for i, constraint in enumerate(constraints):
          if "<=" in constraint:
              explanation += f"{constraint.replace('<=', '+ s')}{i+1} = {constraint.split('<=')[1].strip()}, "
          elif ">=" in constraint:
              explanation += f"{constraint.replace('>=', '- s')}{i+1} = {constraint.split('>=')[1].strip()}, "
          else:
              explanation += f"{constraint}, "
      
      explanation += "x >= 0, y >= 0, s1 >= 0, s2 >= 0. \n3. Create a tableau with coefficients and constraints. \n4. Iteratively use the Simplex algorithm to move from a vertex to the next until an optimum solution is reached. \n5. Determine the optimum solution by inspecting the values in the tableau."
      
      return explanation