Functions that separate points

In summary, the conversation discusses three different definitions of a separating family of seminorms. The first two definitions are equivalent and state that for a family of seminorms, if all seminorms are zero then the element must also be zero. The third definition states that for a family of functions, there exists a function for any two distinct points that can tell them apart. Condition (iii) is usually used when the family of functions needs to differentiate between any pair of elements, but it is too strong in the context of seminorms because linearity allows for differentiation between any two elements. The first two definitions can be seen as generalizations of the axiom of a norm, while the third definition is related to the
  • #1
julian
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In the context of families of seminorms I've come across these two definitions;

i) a family of seminorms [itex]\{ p_I \}[/itex] is separating if [itex]p_I = 0[/itex] for all [itex]I[/itex] implies [itex]x=0[/itex].

ii) for a family of seminorms, when for every [itex]x \in X / \{ 0 \}[/itex] there is a seminorm [itex]p_I[/itex] such that [itex]p_I (x) > 0[/itex].

It is easy to show these imply each other. I have now come across another definition for a family of functions to be separating (not necessarily seminorms):

iii) a family of functions [itex]\{ p_I \}[/itex] is separating if for each pair of points [itex]x \not= y[/itex] we find [itex]p_I[/itex] such that [itex]p_I (x) \not= p_I (y)[/itex]

It is easy to show iii) implies ii) if you assume the functions of condition iii) satisfy [itex]p_I (0) = 0[/itex]. My question is how condition iii) could be implied by either of the others.
 
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  • #2
(iii) is not the same kind of separation as that in (i) and (ii). To see the difference just take the real line and let the family of seminorms be the set consisting of just the ordinary absolute value norm. Then clearly (i) holds because |x|=0 implies x=0, but (iii) does not hold because [itex]1\not= -1[/itex], but |1|=|-1|.

In fact if you take as a family of seminorms just a single seminorm p, then (i) or (ii) holds precisely if p is a norm, but (iii) only holds when the underlying vector space consists of just 0 (otherwise pick non-zero x and we get [itex]x\not= -x[/itex], but p(x)=p(-x)).

Separation is a nice intuitive geometric concept so the term has been overloaded to mean different things in different contexts (just as other terms like regular, normal, space, linear, etc. mean many things).

Your condition (iii) is only usually used when we need our family of functions to tell the difference between any pair of elements, but in a vector space this is not necessary because if our family tells us that there is some kind of separation between 0 and x-y, then there must also be separation between x and y by linearity, but as the seminorms themselves are not linear it is way too strong to expect to say that elements are the same if they have the same norm. You just want to say that elements are separated if their difference has a different seminorm than 0.

In particular you may think about the real line (or plane, or [itex]\mathbb{R}^n[/itex]) where we cannot tell the difference between 1 and -1 with our norm, but the way to think about it is that we want to translate and instead tell the difference between 0 and 2.

You may think of condition (i) and (ii) as being generalizations of the axiom of a norm that |x|=0 implies x=0.

In slightly more technical terms we sometimes call a topological space separated if it is Hausdorff. A locally convex topological vector space is given precisely by a vector space with a family of seminorms which induces the topological structure. The resulting topological vector space is Hausdorff if and only if it is induced by a separating family of seminorms.
 

1. What are functions that separate points?

Functions that separate points are mathematical equations that describe a boundary or line that separates one set of points from another set. They can be represented in various ways, such as equations, graphs, or tables.

2. How do functions that separate points work?

Functions that separate points work by taking in input values and mapping them to specific output values. These output values determine whether a point is on one side or the other of the boundary. For example, a point with a positive output value may be on one side of the boundary, while a point with a negative output value may be on the other side.

3. What are some real-life applications of functions that separate points?

Functions that separate points have various real-life applications, such as in machine learning for classification tasks, in economics for modeling supply and demand, and in physics for describing electric fields and magnetic fields.

4. Can functions that separate points be used for non-linear boundaries?

Yes, functions that separate points can be used for non-linear boundaries, such as circles, ellipses, or any other non-linear shape. This can be achieved by using non-linear equations or transformations on the input variables.

5. How can functions that separate points be visualized?

Functions that separate points can be visualized in various ways, such as by plotting them on a graph, using contour plots to show the boundary, or creating 3D plots to show the separation in three dimensions. They can also be visualized using color maps, where points on one side of the boundary are assigned one color and points on the other side are assigned a different color.

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