Separating hyperplane theorem for non-disjoint sets

In summary, the sets ##X## and ##Y## are compact and convex, but not disjoint. The y-axis can serve as a separating hyperplane in a weak sense for these sets. The Hahn-Banach theorem can be applied to the interiors of the sets.
  • #1
economicsnerd
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Consider the sets ##X:= \{x\in\mathbb R^2: \enspace ||x-(-1,0)||_2 \leq 1\}## (a ball) and ##Y:=co\{(0,-1), (0,1), (1,0)\}## (a triangle).

Both ##X## and ##Y## are compact and convex, but they aren't disjoint: ##X\cap Y = \{(0,0)\}##. Since they aren't disjoint, the most common separating hyperplane/Hahn-Banach theorems don't directly apply. However, the y-axis is a hyperplane which separates them in a weak sense.

Can anybody point me to source for a separating hyperplane theorem that covers this example? Ideally, I'm looking for something that isn't restricted to finite dimensions.

Thanks!
 
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  • #2
economicsnerd said:
Consider the sets ##X:= \{x\in\mathbb R^2: \enspace ||x-(-1,0)||_2 \leq 1\}## (a ball) and ##Y:=co\{(0,-1), (0,1), (1,0)\}## (a triangle).

Both ##X## and ##Y## are compact and convex, but they aren't disjoint: ##X\cap Y = \{(0,0)\}##. Since they aren't disjoint, the most common separating hyperplane/Hahn-Banach theorems don't directly apply. However, the y-axis is a hyperplane which separates them in a weak sense.

Can anybody point me to source for a separating hyperplane theorem that covers this example? Ideally, I'm looking for something that isn't restricted to finite dimensions.

Thanks!

Can't you just apply Hahn-Banach to the interiors?
 
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  • #3
Oh jeez. Now I'm embarrassed.

Thank you, micromass!

Can we strike this one from the records? :P
 

What is the separating hyperplane theorem for non-disjoint sets?

The separating hyperplane theorem for non-disjoint sets is a mathematical theorem that states that for any two non-disjoint convex sets in a vector space, there exists a hyperplane that separates the two sets.

Why is the separating hyperplane theorem important in science?

The separating hyperplane theorem is important in science because it provides a mathematical tool for studying and analyzing non-disjoint sets, which are commonly encountered in many scientific fields such as economics, biology, and engineering.

What are some applications of the separating hyperplane theorem?

The separating hyperplane theorem has various applications in fields such as optimization, game theory, and pattern recognition. It is also used in machine learning algorithms for classification and clustering.

What are the assumptions of the separating hyperplane theorem for non-disjoint sets?

The separating hyperplane theorem assumes that the sets are convex and non-disjoint, and that the vector space is finite-dimensional. Additionally, the theorem requires the sets to be closed, bounded, and have non-empty interiors.

Are there any limitations to the separating hyperplane theorem?

Yes, the separating hyperplane theorem has some limitations. It only applies to convex sets, and it does not guarantee that the separating hyperplane will be unique. In some cases, the separating hyperplane may not exist at all.

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