Post-optimality analysis: Change in one of the constraints

In summary, the LP problem given has an optimal solution of (0,1). Changing the first constraint to either (1) max \, (2x_1+x_2,0) \leq 3 or (2) max \, (2x_1+x_2,6)\leq 3 does not change the optimal solution, as (1) is equivalent to the original constraint and (2) is infeasible.
  • #1
drawar
132
0

Homework Statement



Consider the LP:

max [itex]\, -3x_1-x_2[/itex]
[itex]\,\,[/itex]s.t. [itex]\,\,\,\,[/itex] [itex]2x_1+x_2 \leq 3[/itex]
[itex]\quad \quad \ -x_1+x_2 \geq 1[/itex]
[itex]\quad \quad \quad \quad \ x_1,x_2 \geq 0[/itex]Suppose I have solved the above problem for the optimal solution. (I used dual simplex and get (0,1) as the optimal solution.)
Now if the first constraint [itex](2x_1+x_2 \leq 3)[/itex] is either changed to

(1) max [itex]\, (2x_1+x_2,0) \leq 3[/itex], or
(2) max [itex]\, (2x_1+x_2,6)\leq 3[/itex],

is it possible to obtain the new optimal solution without having to solve the entire problem from the scratch?

Homework Equations


The Attempt at a Solution

I have tried introducing a new variable t to address the maximum and rewrite the constraints in linear form but it doesn't seem to help.

Any hint or comment is greatly appreciated, thank you!
 
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  • #2
Your optimal solution of ##(0,1)## is correct for the first part yielding a max of ##-1##. Could you elaborate on conditions (1) and (2) a bit more? I'm not quite sure what the ##', 0)'## and ##', 6)'## translate to.
 
  • #3
conditions (1) should read "the larger of [itex](2x_1+x_2)[/itex] and [itex]0[/itex] is smaller than 3", that is, if [itex]0 \leq 2x_1+x_2[/itex] then we also have [itex]0 \leq 2x_1+x_2 \leq 3[/itex], otherwise, if [itex]2x_1+x_2 \leq 0[/itex] then [itex]2x_1+x_2 \leq 0[/itex].
 
  • #4
drawar said:

Homework Statement



Consider the LP:

max [itex]\, -3x_1-x_2[/itex]
[itex]\,\,[/itex]s.t. [itex]\,\,\,\,[/itex] [itex]2x_1+x_2 \leq 3[/itex]
[itex]\quad \quad \ -x_1+x_2 \geq 1[/itex]
[itex]\quad \quad \quad \quad \ x_1,x_2 \geq 0[/itex]


Suppose I have solved the above problem for the optimal solution. (I used dual simplex and get (0,1) as the optimal solution.)
Now if the first constraint [itex](2x_1+x_2 \leq 3)[/itex] is either changed to

(1) max [itex]\, (2x_1+x_2,0) \leq 3[/itex], or
(2) max [itex]\, (2x_1+x_2,6)\leq 3[/itex],

is it possible to obtain the new optimal solution without having to solve the entire problem from the scratch?



Homework Equations





The Attempt at a Solution




I have tried introducing a new variable t to address the maximum and rewrite the constraints in linear form but it doesn't seem to help.

Any hint or comment is greatly appreciated, thank you!

Since 3 > 0 the constraint ##\max(2x_1+x_2,0) \leq 3## is the same as the simple constraint ##2x_1 + x_2 \leq 3##; can you see why? Anyway, the solution (1,0) satisfies it, so there would be no change in the solution.

The second form ##\max(2x_1+x_2,6)\leq 3## is infeasible. Can you see why no ##x_1, x_2## can possibly satisfy that inequality? (If you changed '##\max##' to '##\min##' you could satisfy it. Again, can you see why?)
 
  • #5
drawar said:
conditions (1) should read "the larger of [itex](2x_1+x_2)[/itex] and [itex]0[/itex] is smaller than 3", that is, if [itex]0 \leq 2x_1+x_2[/itex] then we also have [itex]0 \leq 2x_1+x_2 \leq 3[/itex], otherwise, if [itex]2x_1+x_2 \leq 0[/itex] then [itex]2x_1+x_2 \leq 0[/itex].

If ##2x_1 + x_2 ≤ 0##, there is no maximum.

You simply can't use the second condition.
 
  • #6
Thank you all, I think I've got what you said,
for (1) since it was given that [itex]x_1,x_2 \geq 0[/itex], [itex]2x_1+x_2 \geq 0[/itex] hence the constraint is equivalent to [itex]2x_1+x_2 \leq 3[/itex].

for (2) whatever the larger is, the constraint makes no sense because 6 > 3, so the problem is infeasible. If 'max' were changed to 'min' then the constraint is nothing but [itex]2x_1+x_2 \leq 3[/itex].
 

Related to Post-optimality analysis: Change in one of the constraints

1. What is post-optimality analysis?

Post-optimality analysis is a technique used in mathematical optimization to evaluate the effects of changes in one of the constraints on the overall optimal solution.

2. Why is post-optimality analysis important?

Post-optimality analysis allows for a better understanding of the sensitivity of the optimal solution to changes in the constraints. It can help identify which constraints have the most impact on the solution and guide decision-making in real-world applications.

3. How is post-optimality analysis performed?

Post-optimality analysis involves solving the optimization problem multiple times, each time with a different constraint value. The resulting optimal solutions are then compared to determine how sensitive the solution is to changes in the constraint.

4. What are some common applications of post-optimality analysis?

Post-optimality analysis can be applied in various fields such as operations research, engineering, economics, and finance. It can be used to optimize production processes, resource allocation, financial portfolios, and more.

5. What are the limitations of post-optimality analysis?

Post-optimality analysis assumes that the problem is linear and that the constraints can be easily changed. It can also be time-consuming and computationally intensive, especially for large-scale optimization problems. Additionally, the results of post-optimality analysis may not be accurate if the problem is highly non-linear or if the constraints cannot be easily adjusted.

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