Testing optimality via complementary slackness

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In summary, the conversation discusses the difficulty in understanding a topic related to a primal LP and determining if a given point x is optimal for the LP. The speaker mentions searching for a step-by-step explanation but only finding information about using the weak duality theorem. The main confusion seems to be about the pivoting process and obtaining y from a given x.
  • #1
zfolwick
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I can't for the life of me understand this topic. given a point x =(1,1,1,1,1,1,1) and a primal LP, does the point solve the primal? An internet search revealed no answer to my question, only criteria which involves knowing y. I am aware that [itex]\sum[/itex]aijxj < b then yi = 0, so I at least know which elements of y are zero, but the rest of the steps elude me.
 
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  • #2
zfolwick said:
I can't for the life of me understand this topic. given a point x =(1,1,1,1,1,1,1) and a primal LP, does the point solve the primal? An internet search revealed no answer to my question, only criteria which involves knowing y. I am aware that [itex]\sum[/itex]aijxj < b then yi = 0, so I at least know which elements of y are zero, but the rest of the steps elude me.

you need to be more clear. Explain in more detail.
 
  • #3
given a point x =(a1, a2, ... am), is this point optimal for a given LP?

Is there a good, step-by-step description of this somewhere? The only thing I can find is that, if I have a point x and a point y, then I can use the weak duality theorem to say that cx = by or some such nonsense.

what I'm really trying to understand is the pivoting process wherein I *get* y from a given point x.
 

1. What is "Testing optimality via complementary slackness"?

"Testing optimality via complementary slackness" is a mathematical principle used in optimization problems to determine the optimal solution. It states that if a pair of variables satisfy certain conditions known as complementary slackness, then they are both optimal.

2. How does complementary slackness work?

Complementary slackness works by comparing the values of the constraints and the objective function in an optimization problem. If the value of a constraint is equal to its upper or lower bound, then its corresponding variable must be zero. This is known as complementary slackness.

3. What are the conditions for complementary slackness?

The conditions for complementary slackness are that the optimal value of a constraint must be equal to its upper or lower bound, and the optimal value of the corresponding variable must be zero. In other words, the constraint must be either active or inactive at the optimal solution, and the corresponding variable must be either positive or zero.

4. How is complementary slackness used in optimization problems?

Complementary slackness is used in optimization problems to determine the optimal solution. By checking the conditions of complementary slackness, we can determine which variables are active and which are inactive at the optimal solution. This helps in simplifying the problem and finding the optimal solution more efficiently.

5. What are the benefits of using complementary slackness in optimization problems?

The benefits of using complementary slackness in optimization problems include simplifying the problem and reducing the number of variables to be considered. It also helps in identifying the optimal solution more efficiently and can be used to check the optimality of a solution found through other methods. Additionally, it provides insight into the sensitivity of the optimal solution with respect to changes in the constraints and objective function.

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