Convergence in the Hausdorff metric

In summary, we have a metric space (X,d) and a nested family of non-empty compact subsets {An} of X. The intersection of all An, denoted A, is also non-empty and compact. We then show that An converges to A in the Hausdorff metric (D), defined as the infimum of all t greater than or equal to 0 such that A is contained in the t-parallel body of B and B is contained in the t-parallel body of A. We use this to show that for any epsilon greater than 0, there exists an N such that A_N is a subset of A_epsilon. Assuming this is not true, we can obtain a contradiction by finding a sequence of
  • #1
math8
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Let (X,d) be a metric space. Let {An} be a nested family of non empty compact subsets of X. Let A=Intersection of all An.
We have that A is non empty and compact.

We show An converges to A in the Hausdorff metric (D).

I know D(A,B)= Inf {t>or eq.0 : A C B_t and B C A_t} Where A_t is the t-parallel body of A meaning A_t={x in X: d(x,A) < or eq.t}.

But I am not sure how to proceed.
 
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  • #2
Haven't thought this through completely, but some thoughts:

Let epsilon > 0.

Show there exists N such that [tex]A_N\subset A_{\epsilon}[/tex].

Suppose not. Get sequence of x_n in A_n but not in A_epsilon, then thin to convergent subsequence.

Obtain contradiction.
 

1. What is convergence in the Hausdorff metric?

Convergence in the Hausdorff metric is a mathematical concept that defines the behavior of a sequence of sets. It describes the idea that as the elements of a sequence of sets get closer and closer to each other, they eventually become indistinguishable. This concept is often used in the study of topology and geometry.

2. How is convergence in the Hausdorff metric different from other types of convergence?

Convergence in the Hausdorff metric is unique in that it measures the distance between sets, rather than individual points. This allows for a more general definition of convergence, as sets can contain an infinite number of points. It also takes into account the shape and structure of the sets, rather than just their elements.

3. What are some real-world applications of convergence in the Hausdorff metric?

Convergence in the Hausdorff metric has practical applications in fields such as computer vision, image processing, and pattern recognition. It can be used to compare and match different shapes and objects, which is useful in tasks such as object recognition and tracking.

4. How is convergence in the Hausdorff metric calculated?

To calculate convergence in the Hausdorff metric, the distance between two sets is measured by finding the closest point in one set to any point in the other set. This process is repeated for both sets, and the maximum distance between these closest points is taken as the distance between the sets. The sequence of sets is considered to converge if this distance approaches 0 as the number of sets in the sequence increases.

5. What are the limitations of convergence in the Hausdorff metric?

Convergence in the Hausdorff metric can be a useful tool for comparing and matching sets, but it does have limitations. It does not take into account the orientation or rotation of sets, and it can be sensitive to outliers. Additionally, the definition of convergence can differ depending on the choice of metric, so care must be taken in selecting an appropriate metric for the problem at hand.

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