Projections from Tubular Neighborhoods

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In summary, the discussion revolves around a problem involving a smooth, compact embedded submanifold M and a tubular neighborhood U. It is known that the projection function r is a smooth retraction, and the goal is to prove that for any point y in a sufficiently small tubular neighborhood U, r(y) is the point on M that minimizes the distance from y. The conversation includes hints from Lee, and eventually leads to a solution involving a curve joining y and a point p on M, and using the convexity of a function to prove the desired result.
  • #1
Goklayeh
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Hello! Could anybody give me some hint with the following problem? Consider a smooth, compact embedded submanifold [itex]M = M^m\subset \mathbb{R}^n[/itex], and consider a tubular neighborhood [itex]U = E(V)\supset M[/itex], where [itex]E: (x, v) \in NM \mapsto x + v \in M[/itex] is a diffeomorphism from a open subset of the normal bundle [itex]NM[/itex] of the form [itex]V = \left\{(x, v) \in NM \: : \: \left| v \right| < \delta\right\}[/itex]. We know that [itex]r:=\pi \circ E^{-1}[/itex] is a smooth retraction, where [itex]\pi:(x, v) \in NM \mapsto x \in M[/itex] is the projection. How can I prove that if [itex]y \in U[/itex], where [itex]U[/itex] is a sufficiently small tubular neighborhood, then [itex]r(y)[/itex] realizes the minimum of the distance from the points of [itex]M[/itex]? I just proved, following Lee's hints, that if [itex]y \in \mathbb{R}^n[/itex] has a closest point [itex]x \in M[/itex], then [itex]y - x \in N_x M[/itex], but I can't realize how to use this information!
 
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  • #2
Maybe, I've solved: let [itex]y = x + v[/itex], for some [itex](x, v) \in V[/itex], and consider any other point [itex]p \in M[/itex], together with a curve [itex]\gamma: (-\epsilon, \epsilon) \to M[/itex] such that [itex]\gamma(0) = x, \dot{\gamma}(0) = v[/itex] joining [itex]x, p[/itex]. Without loss of generality, [itex]y = 0[/itex]. If [itex]f(t):=\frac{1}{2}\left|\gamma(t)\right|^2[/itex], then
[tex]\dot{f}(0) = \left<\gamma(0), \dot{\gamma}(0)\right> = \left<x, v\right> = 0[/tex]
since [itex]y - r(y) = x + v - x = v \in N_x M[/itex]. Since [itex]f[/itex] is convex, [itex]0[/itex] is a minimum, hence the thesis for the arbitrariness of the curve [itex]\gamma[/itex]. Am I wrong?
 

What is the concept of "Projections from Tubular Neighborhoods"?

"Projections from Tubular Neighborhoods" refers to a mathematical concept used in the field of topology, which involves projecting a higher-dimensional space onto a lower-dimensional space. It is often used to study the behavior of curves or surfaces in a given space.

How is the projection from a tubular neighborhood different from a regular projection?

The main difference between a projection from a tubular neighborhood and a regular projection is that the former preserves more of the geometric structure of the object being projected. This is because a tubular neighborhood is a small, local region around the object, rather than the entire space.

What is the significance of studying projections from tubular neighborhoods?

Studying projections from tubular neighborhoods allows for a better understanding of the behavior and properties of objects in higher-dimensional spaces. It also has practical applications in fields such as computer graphics, where projections are used to create 3D images from 2D data.

Can projections from tubular neighborhoods be applied to any type of object?

Yes, projections from tubular neighborhoods can be applied to any type of object, as long as it can be represented mathematically in a higher-dimensional space. This includes curves, surfaces, and even more complex shapes.

Are there any limitations to using projections from tubular neighborhoods?

One limitation of using projections from tubular neighborhoods is that they may not accurately represent the entire higher-dimensional space. This is because they only capture a small, local region around the object being projected. Additionally, the choice of tubular neighborhood can affect the results of the projection and must be carefully considered.

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