MHB The distance is attained by a unique point

  • Thread starter Thread starter evinda
  • Start date Start date
  • Tags Tags
    Point
Click For Summary
In a Hilbert space, if K is a convex and closed subset, there exists a unique point y_x in K that minimizes the distance to a point x in the space. If K is not closed, the minimum distance may not be attained, as illustrated by the example of an open circle in R^2 where the closest point to (2,0) is (1,0), which is not included in K. The uniqueness of the closest point fails if K is not convex, as multiple points can be equidistant from x. A sequence of points in K can converge to the infimum of distances, demonstrating that the distance approaches a limit as the sequence is refined. The discussion emphasizes the importance of convexity and closure in determining the properties of distance minimization in Hilbert spaces.
evinda
Gold Member
MHB
Messages
3,741
Reaction score
0
Hello! (Wave)

Theorem:Let $K$ be a convex and closed subset of a Hilbert space $X$ and $x \in X$. Then there is a unique $y_x \in K$ such that $$||x-y_x||=d(x,K):=inf \{||x-y||: y \in K \}$$

Remarks:

- if $K$ is not closed then the distance $d(x,K)$ isn't attained in general.

If for example $X=\mathbb{R}^2$ and $D(0,1)=\{ y \in \mathbb{R}^2: ||y||_2 <1\}$ and $x=(2,0)$ then $d(x,K)=1$ and is attained at the point $(1,0) \notin K$.Could you explain me how we deduce that $d(x,K)=1$ ? - if $K$ isn't convex then the uniqueness isn't satisfied.

Could you explain me why? - If $A \subset \mathbb{R} (\neq \varnothing)$ then there is a sequence $(a_n) \in A$ such that $a_n \to \inf A$ .
From the last remark we have that there is a sequence $(y_n) \in K$ such that $||x-y_n|| \to d(x,K)$.

If we apply the last remark don't we get that there is a $y_n \in K$ such that $y_n \to \inf K$ ? How do we conclude that $||x-y_n|| \to d(x,K)$ ?Firstly we want to show that $(y_n)$ is Cauchy and since $X$ is a complete metric space it will converge to a $y \in X$, i.e. $||y_n-y|| \to 0$ and since $K$ is closed we will have that $y \in K$.In order to show this we will use the Parallelogram law $||x-y||^2+||x+y||^2=2 ||x||^2+2 ||y||^2$. But what $x$ and $y$ do we have to use? (Thinking)
 
Physics news on Phys.org
I don't have time to answer all your questions just now, but I can answer a few, and perhaps nudge you on the others.

evinda said:
Hello! (Wave)

Theorem:Let $K$ be a convex and closed subset of a Hilbert space $X$ and $x \in X$. Then there is a unique $y_x \in K$ such that $$||x-y_x||=d(x,K):=inf \{||x-y||: y \in K \}$$

Remarks:

- if $K$ is not closed then the distance $d(x,K)$ isn't attained in general.

If for example $X=\mathbb{R}^2$ and $D(0,1)=\{ y \in \mathbb{R}^2: ||y||_2 <1\}$ and $x=(2,0)$ then $d(x,K)=1$ and is attained at the point $(1,0) \notin K$.Could you explain me how we deduce that $d(x,K)=1$ ?

So $K$ is the open circle of radius $1$, centered at the origin, right? If you visualize the point $(2,0)$ relative to the circle, then the point on the boundary of the circle closest to $x$ is $(1,0)$. However, this point is not in the open circle. Consider the sequence $\{(1-1/n,0)\} \subset K$, and the distance from each of these points to $(2,0)$ in the Euclidean metric. Then those distances are $2-(1-1/n)=1+1/n$. If we take the infimum of these distances, we will see that it is $1$, and hence $d(x,K)=1$.
- if $K$ isn't convex then the uniqueness isn't satisfied.

Could you explain me why?

See the following figure:

View attachment 4982

The point on the right is equidistant from the two different lobes on the left. The two circles on the left make a decidedly non-convex shape, and so you can see why the distance from a point to a set might not be unique if the set isn't convex.

The rest of your post will take considerably more thought. I can't promise a time when I can look at it.
 

Attachments

  • Non-Uniqueness of Distance for Non-Convex Sets.png
    Non-Uniqueness of Distance for Non-Convex Sets.png
    686 bytes · Views: 113
evinda said:
Theorem:Let $K$ be a convex and closed subset of a Hilbert space $X$ and $x \in X$. Then there is a unique $y_x \in K$ such that $$||x-y_x||=d(x,K):=inf \{||x-y||: y \in K \}$$
.
.
.

- If $A \subset \mathbb{R} (\neq \varnothing)$ then there is a sequence $(a_n) \in A$ such that $a_n \to \inf A$ .

From the last remark we have that there is a sequence $(y_n) \in K$ such that $||x-y_n|| \to d(x,K)$.

Firstly we want to show that $(y_n)$ is Cauchy and since $X$ is a complete metric space it will converge to a $y \in X$, i.e. $||y_n-y|| \to 0$ and since $K$ is closed we will have that $y \in K$.In order to show this we will use the Parallelogram law $||x-y||^2+||x+y||^2=2 ||x||^2+2 ||y||^2$. But what $x$ and $y$ do we have to use? (Thinking)
In the parallelogram law, replace "$x$" by $x-y_n$ and "$y$" by $x-y_m$. Then the law says that $$\|(x-y_n) - (x-y_m)\|^2 + \|(x-y_n) + (x-y_m)\|^2 = 2\|x-y_n\|^2 + 2\|x-y_m\|^2.$$ On the left side of that equation, the first term is $\|y_m-y_n\|^2$. The second term can be written as $\|2x - (y_m + y_n)\|^2 = 4\|x - \frac12(y_m+y_n)\|^2.$ But $\frac12(y_m+y_n) \in K$ because $K$ is convex. Therefore $\|x - \frac12(y_m+y_n)\| \geqslant d = d(x,K)$. It follows that $$\|y_m-y_n\|^2 \leqslant 2\|x-y_n\|^2 + 2\|x-y_m\|^2 - 4d^2.$$ When $m$ and $n$ are large, $\|x-y_n\|$ and $\|x-y_m\|$ are both close to $d$, and it follows that the right side of that inequality will be close to zero. It follows that $\|y_m-y_n\|^2$ on the left side of the inequality will also be close to zero. This shows that the sequence $(y_n)$ is Cauchy.
 
Ackbach said:
So $K$ is the open circle of radius $1$, centered at the origin, right? If you visualize the point $(2,0)$ relative to the circle, then the point on the boundary of the circle closest to $x$ is $(1,0)$. However, this point is not in the open circle. Consider the sequence $\{(1-1/n,0)\} \subset K$, and the distance from each of these points to $(2,0)$ in the Euclidean metric. Then those distances are $2-(1-1/n)=1+1/n$. If we take the infimum of these distances, we will see that it is $1$, and hence $d(x,K)=1$.

Could you explain it further to me? (Sweating)

Ackbach said:
See the following figure:
The point on the right is equidistant from the two different lobes on the left. The two circles on the left make a decidedly non-convex shape, and so you can see why the distance from a point to a set might not be unique if the set isn't convex.

Ah, I see... (Nod)
 
evinda said:
Could you explain it further to me? (Sweating)

Sure! Let me show you a picture:

View attachment 4990

Here I've included the Mathematica code I used to generate this plot. Now, the circle is shown filled, with a dotted line, to show that the boundary itself is not included in the circle (it's an open circle). The point is shown on the right with a blue dot. We're trying to find the distance from the point to the circle. Now, imagine any sequence of points converging to the right-most point of the circle, which is at $(1,0)$, and is clearly the point on the (closed) circle closest to the point $(2,0)$. The infimum of the distances from the sequence points to the point $(2,0)$ will be $1$, which is simply the Euclidean distance from $(1,0)$ to $(2,0)$.

Does that answer your question?
 

Attachments

  • Evinda Analysis Question - Distance Circle Point.png
    Evinda Analysis Question - Distance Circle Point.png
    8.3 KB · Views: 121
Ackbach said:
Sure! Let me show you a picture:
Here I've included the Mathematica code I used to generate this plot. Now, the circle is shown filled, with a dotted line, to show that the boundary itself is not included in the circle (it's an open circle). The point is shown on the right with a blue dot. We're trying to find the distance from the point to the circle. Now, imagine any sequence of points converging to the right-most point of the circle, which is at $(1,0)$, and is clearly the point on the (closed) circle closest to the point $(2,0)$. The infimum of the distances from the sequence points to the point $(2,0)$ will be $1$, which is simply the Euclidean distance from $(1,0)$ to $(2,0)$.

Does that answer your question?

Yes, I got it... Thanks a lot! (Smirk)
 
We all know the definition of n-dimensional topological manifold uses open sets and homeomorphisms onto the image as open set in ##\mathbb R^n##. It should be possible to reformulate the definition of n-dimensional topological manifold using closed sets on the manifold's topology and on ##\mathbb R^n## ? I'm positive for this. Perhaps the definition of smooth manifold would be problematic, though.

Similar threads

  • · Replies 0 ·
Replies
0
Views
2K
  • · Replies 8 ·
Replies
8
Views
3K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
Replies
2
Views
2K
  • · Replies 4 ·
Replies
4
Views
1K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 4 ·
Replies
4
Views
1K