A separation theorem in locally convex spaces

In summary, the conversation discusses a theorem from a book about separation of convex sets in a normed space. The theorem is then generalized to locally convex topological linear spaces, but the proof is not shown. The conversation also mentions a lemma that is used in the proof and some generalizations of the original theorem. The final part of the conversation introduces a new theorem about separating convex sets by a hyperplane in a vector space, and provides a proof for it.
  • #1
DavideGenoa
155
5
Dear friends, my book (an Italian language translation of Kolmogorov-Fomin's Элементы теории функций и функционального анализа) proves the following separation theorem: let ##A## and ##B## be convex sets of a normed space and let ##A## have a non-empty algebraic interior ##J(A)## such that ##J(A)\cap B =\emptyset##. Then there is a non-null continuous linear functional separating ##A## and ##B##.
Then, with some translation errors or misprints, it says that the theorem can be generalized to locally convex topological linear spaces (Kolmogorov and Fomin don't require them to be ##T_1##). If ##A## is open I easily see that it applies, but I wonder whether it can be generalized as it is stated and hope I can find a proof of it if it can...
##\infty## thanks for any help!
 
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  • #2
So you see how it is true with ##A## open? Well, then just apply the theorem to ##J(A)## and ##B## to find a separating functional ##f:V\rightarrow \mathbb{R}## such that ##f(a)\leq c\leq f(b)## for ##a\in A## and ##b\in B##.

Then we apply the following lemma:
Let ##f:V\rightarrow \mathbb{R}## be any nontrivial functional, let ##A## be any subset of a locally convex vector space ##V## such that ##J(A)\neq \emptyset##. Let ##c\in \mathbb{R}##. Then the following are equivalent:

1) ##f(a)\leq c## for each ##a\in A##
2) ##f(a)\leq c## for each ##a\in J(A)##
3) ##f(a)<c## for each ##a\in J(A)##

##(1)\Rightarrow (2)## is trivial.

##(2)\Rightarrow (3)## Assume by contradiction that there is an ##a\in J(A)## such that ##f(a) = c##. Since ##f## is nontrivial, we can find a ##v\in V## such that ##f(v)>0##. Clearly, there exists some ##t>0## such that ##a+tv\in J(A)##. But then ##f(a+tv)>f(a)=c##, contradicting ##(2)##.

##(3)\Rightarrow (1)## Assume by contradiction that there is some ##x\in A## with ##f(x)> c##. Since ##J(A)## is nonempty, we can take some ##a\in J(A)##. We can thus find a continuous seminorm ##p## such that the open ball ##B_p(a,\varepsilon)## is contained in ##A##. But then we can easily check that ##B_p((1-t)a + tx, (1-t)\varepsilon)\subseteq A## for each ##t\in [0,1)##. So we see that the segment ##[a,x)## is a subset of ##J(A)##. But this segment must intersect ##f^{-1}(c)##. So there must exist some ##z\in J(A)## such that ##f(z) = c##. This is a contradiction.
 
  • #3
Well, so it works. I think [itex]T_2[/itex] is needed, as you use the seminorm in [itex](3)\Rightarrow (1)[/itex]: correct?
Moreover, I don't know why [itex]J(A)[/itex] is open: could you help me to understand it?
[itex]\aleph_1[/itex] thanks!
 
  • #4
DavideGenoa said:
Well, so it works. I think [itex]T_2[/itex] is needed, as you use the seminorm in [itex](3)\Rightarrow (1)[/itex]: correct?
Moreover, I don't know why [itex]J(A)[/itex] is open: could you help me to understand it?
[itex]\aleph_1[/itex] thanks!

Oh, sorry. I missed that you were working with the algebraic interior. The above post is for the topological interior (and it doesn't use Hausdorff).

Here are some generalizations of the OP:

Lemma:
let A be any subset of a vector space V such that ##J(A)\neq \emptyset##. Furthermore, let ##f:V\rightarrow \mathbb{R}## be a nontrivial functional and let ##c\in \mathbb{R}##. Then the following are equivalent:

1) ##f(a)\leq c## for each ##a\in A##
2) ##f(a)\leq c## for each ##a\in J(A)##
3) ##f(a)<c## for each ##a\in J(A)##

##(1)\Rightarrow (2)## is trivial.

##(2)\Rightarrow (3)## Assume by contradiction that there is an ##a\in J(A)## such that ##f(a)=c##. Since ##f## is nontrivial, we can find a ##v\in V## such that ##f(v)>0##. By definition of the algebraic interior, there exists some ##t>0## such that ##a+tv\in A##. But then ##a + \frac{t}{2}v\in J(A)##. Hence ##f(a+ \frac{t}{2}v)>c##, which is a contradiction.

(3)⇒(1) Assume by contradiction that there is some ##x\in A## with ##f(x)>c##. Since ##J(A)## is nonempty, we can take some ##a\in J(A)##. By convexity of ##A##, we see that the segment ##[a,x)## is a subset of ##J(A)##. But this segment must intersect ##f^{-1}(c)##. So there must exist some ##z\in J(A)## such that ##f(z)=c##. This is a contradiction.

Lemma
Let ##L## be a subspace of a vector space ##V## and let ##A## be a convex, absorbing set. Let ##f:L\rightarrow \mathbb{R}## be a linear functional such that there exist an ##m\in \mathbb{R}## such that ##f(y)\leq m## for each ##y\in A\cap L##. Then there exists an extension ##f:V\rightarrow \mathbb{R}## such that ##f(y)\leq m## for ##y\in A##.
Furthermore, if ##V## is a topological vector space and if ##A## is a neighborhood of ##0##, then ##f## is continuous.

Proof:
Let ##p_A## be the Minkowski functional of ##A##. Then ##p_A## is sublinear and
[tex]\{x\in V~\vert~p_A(x)<1\}\subseteq A\subseteq \{x\in V~\vert~p_A(x)\leq 1\}[/tex]
It follows that ##l(y)\leq mp_A(y)## for each ##y\in L##. Indeed, if ##p_A(y)<\alpha##, then ##\frac{1}{\alpha}y\in A##, and thus ##f(\frac{1}{\alpha}y)\leq m##. Hence ##f(y)\leq m\alpha##.
Now, by the algebraic Hahn-Banach theorem, we can find an extension ##f:V\rightarrow \mathbb{R}## such that ##f(y)\leq mp_A(y)## for each ##y\in V##. Thus ##f(y)\leq m## for each ##y\in A##.
The continuity statement follows immediately from the fact that a linear functional on a topological vector space is continuous if and only if it is bounded above on a neighborhood of ##0##.

Lemma
Let ##C## be a convex set in a vector space ##V##. If ##J(C)\neq \emptyset## and ##x_0\notin J(C)##, then ##\{x_0\}## and ##C## can be separated by a hyperplane.
If ##V## is a topological vector space and if ##C## has nonempty topological interior, then we can take the hyperplane closed.

Proof:
By translating, we can suppose WLOG that ##0\in J(C)##. Then ##C## is absorbing and ##x_0\neq 0##. Consider the subspace ##L=\mathbb{R}x_0##. The algebraic interior of the interval ##L\cap C## contains ##0## but not ##x_0## (otherwise, ##x_0## would belong to ##J(C)##. Thus the linear functional ##f:L\rightarrow \mathbb{R}: tx_0\rightarrow t## satisfies that ##f(y)\leq f(x_0)## whenever ##y\in C\cap L##. By the previous lemma, there exist a linear extension ##f:V\rightarrow \mathbb{R}## such that ##f(y)\leq f(x_0)## for each ##y\in C##.

Finally, the theorem:
Let ##A## and ##B## be two nonempty convex sets in a vector space ##V##. If ##J(A)\neq \emptyset## and ##B\cap J(A) = \emptyset##, then ##A## and ##B## can be separated by a hyperplane.
Moreover if ##V## is a topological vector space, if ##A## has nonempty topological interior, then we can take the hyperplane to be closed.

Proof:
The set ##J(A)## is convex, nonempty and disjoint from ##B##. The set ##C = J(A) - B## is then convex, has nonempty algebraic interior and does not contain ##0##. By previous lemma, there exists a nontrivial linear functional ##f## on ##V## such that ##f(c)\leq 0## for each ##c\in C##. It follows that ##f(a)\leq f(b)## for each ##a\in J(A)## and ##b\in B##. The theorem now follows by application of the first lemma in this post.
 
  • #5
Wow, what a detailed answer! Thank you so much! As to the lemma Let [itex]L[/itex] be a subspace... I understand that [itex]\alpha>p_A(y)\Rightarrow\alpha^{-1}y\in A[/itex], but it isn't clear to me why [itex]f(p_A(y)^{-1} y)\leq m[/itex]. I suppose that [itex]p_A(y)^{-1}y\in A[/itex] but I don't understand why, since [itex]p_A[/itex] represents an infimum... [itex]\aleph_1[/itex] thanks!
 
  • #6
DavideGenoa said:
Wow, what a detailed answer! Thank you so much! As to the lemma Let [itex]L[/itex] be a subspace... I understand that [itex]\alpha>p_A(y)\Rightarrow\alpha^{-1}y\in A[/itex], but it isn't clear to me why [itex]f(p_A(y)^{-1} y)\leq m[/itex].

Is your problem that you don't how from ##p_A(y)<\alpha~\Rightarrow~f(y)<m\alpha## follows that ##f(y)\leq mp_A(y)##?

To prove it, assume that ##p_A(y) < \frac{1}{m} f(y)##. Choose a number ##\alpha>0## such that ##p_A(y)<\alpha<\frac{1}{m}f(y)##. Then since ##p_A(y)<\alpha##, it follows immediately from the hypothesis that ##f(y)<m\alpha##. And thus ##\alpha<\frac{1}{m}f(y)<\alpha##. This is a contraduction.
 
  • #7
[itex]\aleph_2[/itex] thanks! I had to increase cardinality :cool:
So, I see that the theorem my book stated holds for any vector space. Since I'm interested in understanding your first proof that in a locally convex space [itex]\forall a\in A\quad f(a)\leq c\iff\forall a\in \text{Int}(A)\quad f(a)\leq c\iff\forall a\in \text{Int}(A)\quad f(a)< c [/itex], while I supposed you used Kadison-Ringrose's theorem 1.2.6, I now think the seminorm you talked about is the Minkowski functional [itex]P_{A-a}[/itex] for [itex]A-a[/itex]: have I understood?
 
  • #8
DavideGenoa said:
[itex]\aleph_2[/itex] thanks! I had to increase cardinality :cool:
So, I see that the theorem my book stated holds for any vector space. Since I'm interested in understanding your first proof that in a locally convex space [itex]\forall a\in A\quad f(a)\leq c\iff\forall a\in \text{Int}(A)\quad f(a)\leq c\iff\forall a\in \text{Int}(A)\quad f(a)< c [/itex], while I supposed you used Kadison-Ringrose's theorem 1.2.6, I now think the seminorm you talked about is the Minkowski functional [itex]P_{A-a}[/itex] for [itex]A-a[/itex]: have I understood?

A locally convex space is a vector space equipped with a collection of seminorms ##\mathcal{P}## which generate the topology. This means that the collection of balls

[tex]B_p(a,\varepsilon),~p\in \mathcal{P},~a\in V,~\varepsilon>0[/tex]

forms a subbasis for the topology. Or equivalently, the sets

[tex]V(a:p_1,...,p_n:\varepsilon) = \{x\in V~\vert~p_i(a-x)<\varepsilon, i\in \{1,...,n\}\}[/tex]

form a basis for each ##a\in V##, ##p_1,...,p_n\in \mathcal{P}## and ##\varepsilon>0##. A basis means that for each open set ##G## and ##a\in G##, there exists some basis open set ##V(a:p_1,...,p_n:\varepsilon)\subseteq G##. If I then define ##p=\max\{p_1,...,p_n\}## then this is also a continuous seminorm and we have that ##B_p(a,\varepsilon)\subseteq G##. This is exactly what I used in my proof.
 
  • #9
I didn't know that: I only knew theorem 1.2.6 of Kadison-Ringrose's Fundamentals of the Theory of Operator Algebras, which is valid for Hausdorff spaces. Thank you so much again!
 
  • #10
DavideGenoa said:
I didn't know that: I only knew theorem 1.2.6 of Kadison-Ringrose's Fundamentals of the Theory of Operator Algebras, which is valid for Hausdorff spaces. Thank you so much again!

Theorem 1.2.6 remains valid for non-Hausdorff spaces if remove the separation condition (which is actually equivalent with Hausdorff). So locally convex spaces can (even in the non-Hausdorff case) be described in two equivalent ways: by the existence of a base of convex set or equivalently, by the existence of a collection of seminorms generating the topology. The proof of this assertion should be exactly the same as the proof of 1.2.6.
 
  • #11
Wonderfully interesting, I heartily thank you! Written a pencil note in the book.
 
  • #12
I wonder when I'll get ##c## amount of thanks :tongue:
 

1. What is a separation theorem in locally convex spaces?

A separation theorem in locally convex spaces is a mathematical result that states that given two non-empty, convex subsets in a locally convex topological vector space, there exists a continuous linear functional that separates the two sets. This means that the two sets can be distinguished by a linear functional, which is a function that maps vectors to real numbers and satisfies certain properties.

2. What is the importance of this theorem?

The separation theorem in locally convex spaces is important in functional analysis and optimization theory. It provides a powerful tool for proving existence and uniqueness of solutions to optimization problems and for studying the properties of convex sets.

3. How does this theorem relate to the Hahn-Banach theorem?

The Hahn-Banach theorem is a generalization of the separation theorem in locally convex spaces. It states that for any two disjoint convex sets in a normed vector space, there exists a continuous linear functional that separates them. Thus, the separation theorem in locally convex spaces can be seen as a special case of the Hahn-Banach theorem in locally convex spaces.

4. Can this theorem be applied to infinite-dimensional spaces?

Yes, the separation theorem in locally convex spaces holds in both finite and infinite-dimensional spaces. However, the proof and the conditions for its application may differ in infinite-dimensional spaces.

5. What are some practical applications of this theorem?

The separation theorem in locally convex spaces has numerous applications in mathematics and engineering, particularly in optimization problems and convex analysis. It is also used in functional analysis to prove the existence and uniqueness of solutions to differential equations and other mathematical models.

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