Revised Simplex Method

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In summary, we used the Revised Simplex Method to find the optimal value and one optimal solution for the given problem. The optimal value is 4 and the optimal solution is x_1 = 4, x_2 = 3/2, x_3 = 0, x_4 = 0. To prove that this is the correct optimal value, we can use the Revised Simplex Method again to verify the solution. Additionally, the solution to the Dual is unique because there is only one optimal solution that satisfies the constraints. This is not surprising as the dual solution is degenerate, meaning that there are more than one basic feasible solutions that satisfy the constraints. However, there is only one primal optimal solution because
  • #1
pinki82
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Use the Revised Simplex Method to find the optimal value and one
optimal solution to the problem,
Max ( x_1 - x_3 )
x_1 - 2x_2 - x_3 + x_4 <= 1
-x_1 + 4x_2 - x_3 <= 2
-x_1 - 3x_2 + x_3 <= 3
x_1, x_2, x_3, x_4 >= 0

b) Prove that the optimal value in a) is correct.
c) Why is the solution to the Dual Unique?
d) Why is it not a surprise that the dual solution is Degenerate?
e) Show that although the dual optimal solution is degenerate, there is
only one primal optimal solution.
f) Also find 6 basic Feasible Solutions.





My work: FOr part a, i got optimal value as 4 and optimal solution x_1=4,
x_2 = 3/2 , x_3 = 0 , x_4 = 0

But i don't understnad how to do other ones..
any hint or help on anyone of them please..thanks a lot!
 
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  • #2


a) To solve this problem using the Revised Simplex Method, we first need to convert the problem into standard form. This can be done by introducing slack variables and converting all inequalities into equations. The standard form of the problem is:

Max ( x_1 - x_3 )
x_1 - 2x_2 - x_3 + x_4 + x_5 = 1
-x_1 + 4x_2 - x_3 + x_6 = 2
-x_1 - 3x_2 + x_3 + x_7 = 3
x_1, x_2, x_3, x_4, x_5, x_6, x_7 >= 0

Next, we need to create the initial tableau by writing down the coefficients of the variables and the objective function. This will help us in determining the pivot element and performing the necessary operations to reach the optimal solution. The initial tableau for this problem is:

| Basic Variables | x_1 | x_2 | x_3 | x_4 | x_5 | x_6 | x_7 | RHS |
| x_1 | 1 | -2 | -1 | 1 | 0 | 0 | 0 | 1 |
| x_2 | -1 | 4 | -1 | 0 | 1 | 0 | 0 | 2 |
| x_3 | -1 | -3 | 1 | 0 | 0 | 1 | 0 | 3 |
| z | 1 | 0 | -1 | 0 | 0 | 0 | 0 | 0 |

To find the optimal value and optimal solution, we need to perform the Revised Simplex Method. This involves selecting the pivot element, performing row operations to make it the leading entry in its column, and then substituting the values of the basic variables into the objective function to determine the optimal value. The optimal solution is then obtained by setting the remaining non-basic variables to 0.

Following the Revised Simplex Method, the optimal value is 4 and the optimal solution is x_1 = 4, x_2 = 3/2, x_3 = 0, x_4 = 0. This can be verified by substituting these values into the objective function, which gives us 4 as the
 

1. What is the Revised Simplex Method?

The Revised Simplex Method is a mathematical algorithm used to solve linear programming problems in which a set of linear equations are optimized by finding the maximum or minimum value of a linear objective function, subject to a set of linear constraints. It is an improved version of the original Simplex Method and is more efficient in terms of computation time and handling degenerate cases.

2. How does the Revised Simplex Method work?

The Revised Simplex Method works by starting with an initial feasible solution and then iteratively improving it until the optimal solution is reached. At each iteration, the method selects a non-basic variable to enter the basis, and then determines which basic variable should leave the basis, thus improving the solution and moving closer to the optimal solution. This process continues until there are no more improvements that can be made, and the optimal solution is reached.

3. What are the advantages of using the Revised Simplex Method?

The Revised Simplex Method has several advantages over the original Simplex Method, including a more efficient handling of degenerate cases, better performance on sparse matrices, and the ability to solve problems with a large number of variables and constraints. Additionally, the Revised Simplex Method has better numerical stability, meaning it is less prone to errors due to rounding and floating-point arithmetic.

4. Are there any limitations to using the Revised Simplex Method?

While the Revised Simplex Method is a powerful tool for solving linear programming problems, it does have some limitations. One limitation is that it can only be used for problems with continuous variables and linear constraints. Additionally, the method may not be efficient for problems with a large number of constraints compared to other methods such as interior-point methods.

5. How does the Revised Simplex Method compare to other optimization methods?

The Revised Simplex Method is one of the oldest and most widely used optimization methods for linear programming problems. It is generally considered to be faster and more efficient than the original Simplex Method, but may not be as efficient as other methods such as interior-point methods for certain types of problems. Ultimately, the choice of optimization method depends on the specific characteristics of the problem and the desired level of accuracy and efficiency.

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