What is the Metric for Convergence in Cartesian Product of Metric Spaces?

In summary, the conversation discusses showing that a sequence converges in the Cartesian product of metric spaces if and only if each component of the sequence converges in its respective space. It also mentions the need to prove that a certain function is a metric on the product space.
  • #1
CornMuffin
55
5

Homework Statement



Let [tex](X_k,d_k)[/tex], [tex]1\leq k<\infty [/tex] be metric spaces
Let [tex]X=\prod _{k=1}^{\infty} X_k [/tex] be their Cartesian product,
that is, let [tex]X[/tex] be the set of sequences [tex](x_1,x_2,...)[/tex], where [tex]x_j\in X_j[/tex] for [tex]1\leq j < \infty [/tex]

Show that a sequence [tex] \left\{x^{(k)}\right\}_{k=1}^{ \infty } [/tex] converges in [tex]X[/tex] if and only if [tex] \left\{x_j^{(k)}\right\}_{k=1}^{ \infty } [/tex] converges in [tex] X_j [/tex] for each [tex] j \geq 1. [/tex]


Homework Equations





The Attempt at a Solution



Assume [tex] \left\{x^{(k)}\right\}_{k=1}^{ \infty } [/tex] converges in [tex]X[/tex].
then [tex] \left\{x^{(k)}\right\}_{k=1}^{ \infty } = (a_{11},a_{21},...),(a_{12},a_{22},...),...[/tex] where [tex] a_{ik} \in X_i [/tex]
So, [tex] \left\{x^{(k)}\right\}_{k=1}^{ \infty } [/tex] converges to [tex]c_i [/tex]

Assume [tex] \left\{x_j^{(k)}\right\}_{k=1}^{ \infty } [/tex] converges in [tex]X_j [/tex] for each [tex] j \geq 1 [/tex]
so, [tex] \left\{x^{(k)}\right\}_{k=1}^{ \infty } = (a_{11},a_{21},...),(a_{12},a_{22},...),...[/tex] where [tex] \left\{x_j^{(k)}\right\}_{k=1}^{ \infty } = a_{j1},a_{j2},...[/tex] converges to [tex]c_j[/tex]
So, [tex] \left\{x^{(k)}\right\}_{k=1}^{ \infty } [/tex] converges to [tex] (c_1,c_2,...) [/tex]
 
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  • #2
Can you tell us what is the metric on [itex]X[/itex]?
 
  • #3
jbunniii said:
Can you tell us what is the metric on [itex]X[/itex]?

I don't think it gives an explicit metric on X, but from part a (this is part b), it asks to show that something is a metric on X, but I didn't think that carried over (but maybe it does)

from part a:
Show that

[tex] d(x,y) = \sum _{j=1}^{ \infty }\frac{1}{2^j}min(1,d_j(x_j,y_j)) [/tex]

is a metric on [tex]X[/tex]

(idk why it kept isametric in the tex tags :(
 
  • #4
CornMuffin said:
I don't think it gives an explicit metric on X, but from part a (this is part b), it asks to show that something is a metric on X, but I didn't think that carried over (but maybe it does)

from part a:
Show that
[tex] d(x,y) = \sum _{j=1}^{ \infty }\frac{1}{2^j}min(1,d_j(x_j,y_j)) [/tex]

is a metric on [tex]X[/tex]

I think it must carry over, because there's more than one way to define a metric on [itex]X[/itex] and it's hard to talk about convergence in a metric space if you don't specify what the metric is. Try proceeding under that assumption and let us know if you get stuck.
 

1. What is a metric space?

A metric space is a mathematical concept that describes a set of points, where the distance between any two points is defined by a function called a metric. This function takes in two points and outputs a non-negative value, representing the distance between those two points.

2. What are the properties of a metric space?

A metric space must satisfy three properties: the distance between any two points must be non-negative, the distance between a point and itself must be zero, and the distance between two points must be equal regardless of the order in which they are given.

3. How is the metric function defined?

The metric function is defined as d(x,y) = |x-y|, where x and y are points in the metric space. This function represents the absolute value of the difference between the two points, which is a non-negative value.

4. What is the significance of open and closed sets in a metric space?

In a metric space, open and closed sets are used to define the topology of the space. Open sets are sets that do not contain their boundary points, while closed sets are sets that contain all their boundary points. These concepts are important in understanding the convergence of sequences and continuity of functions in metric spaces.

5. How are metric spaces used in real-world applications?

Metric spaces have various real-world applications, including in physics, engineering, and computer science. They are used to model physical systems, analyze data sets, and develop algorithms for optimization problems. They are also used in machine learning and artificial intelligence for clustering and classification tasks.

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