Points of the surface with minimum distance to the point (3,0,0)

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Discussion Overview

The discussion revolves around finding points on the surface defined by the equation \( z^2 = 8 + (x-y)^2 \) that have the minimum distance to the point (3,0,0). Participants explore methods for determining this minimum distance, including the use of a function \( f(x,y) = (x-3)^2 + y^2 + (x-y)^2 \) and its critical points.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning

Main Points Raised

  • One participant calculates the gradient of the function \( f \) and finds a critical point at (2,1), asserting it is a minimum based on the Hessian matrix's positive eigenvalues.
  • Another participant questions whether it is possible to show \( f(2,1) \leq f(x,y) \) without using the Hessian matrix, suggesting the function's structure implies it cannot be a maximum or saddle point.
  • Some participants propose that since \( f \) consists of squares, it increases to infinity in all directions, indicating that the critical point must be a minimum.
  • There is a discussion about whether the minimum distance to the point (3,0,0) occurs at the same point as the minimum of \( f \), with one participant affirming this connection.
  • Clarifications are sought regarding the nature of saddle points, with participants discussing the conditions that would define a point as a saddle point versus a minimum.

Areas of Agreement / Disagreement

Participants generally agree that the critical point found is a minimum, but there is ongoing discussion about the nature of saddle points and whether the minimum distance corresponds to the minimum of the function \( f \). The discussion remains unresolved regarding the necessity of the Hessian matrix for proving the minimum condition.

Contextual Notes

Some participants express uncertainty about the implications of using the Hessian matrix and the conditions under which a point can be classified as a saddle point. The discussion does not resolve these nuances.

mathmari
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Hey! :giggle:

We have the function $$f(x,y)=(x-3)^2+y^2+(x-y)^2$$ and I have shown that at $(2,1)$ we have a minimum and so $f(2,1)\leq f(x,y)$ for all $(x,y\in \mathbb{R}^2$.

I did that in this way:
I calculated the gradient and set this equal to zero and found that the only critical point is $(2,1)$. Then I calculated the Hessian matrix, the eigenvalues and since these are positive we get that the critical point is a minimum.
Is this the only way to do that? I mean after having found the critical point could we somehow show directly that $f(2,1)\leq f(x,y)$ for all $(x,y\in \mathbb{R}^2$ without using the Hessian Matrix?

:unsure: Now I want to determine the points of the surface $z^2=8+(x-y)^2$ that have the minimum distance to the point $(3,0,0)$.

I have done the following:

Let $(x,y,z)$ be the point that we are looking for. This point is on the surface, so it satisfies the equation $z^2=8+(x-y)^2$.
The distance to $(3,0,0)$ is $d(x,y,z)=\sqrt{(x-3)^2+y^2+z^2}$.
We can also consider $d^2$ onstead of $d$, or not?
We can substitute $z^2$ by $8+(x-y)^2$, or not?
If yes, then we get $d^2=(x-3)^2+y^2+8+(x-y)^2=(x-3)^2+y^2+(x-y)^2+8=f(x,y)+8$.
So we have the function $f$ plus a constant. Does this mean that the minimum of $d$ is at the same point as the minimum of $f$ ?:unsure:
 
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mathmari said:
Is this the only way to do that? I mean after having found the critical point could we somehow show directly that $f(2,1)\leq f(x,y)$ for all $(x,y\in \mathbb{R}^2$ without using the Hessian Matrix?

Hey mathmari!

The function consists of only squares and it follows that the function value increases to plus infinity in all directions.
In other words, the critical point cannot be a maximum, and it cannot be a saddle point either.
Therefore it must be a minimum. 🤔

mathmari said:
So we have the function $f$ plus a constant. Does this mean that the minimum of $d$ is at the same point as the minimum of $f$ ?

Yep. (Sun)
 
Klaas van Aarsen said:
The function consists of only squares and it follows that the function value increases to plus infinity in all directions.
In other words, the critical point cannot be a maximum, and it cannot be a saddle point either.
Therefore it must be a minimum. 🤔

That it cannot be a maximum is clear to me.
I haven't really understood why it cannot be a saddle point. Could you explain that further to me? :unsure:
 
mathmari said:
That it cannot be a maximum is clear to me.
I haven't really understood why it cannot be a saddle point. Could you explain that further to me?
If it is a saddle point, then there must be a direction where $f$ goes to up, and also a direction where $f$ goes down.
Since $f$ goes to $+\infty$ in all directions, it's not going down. 🤔
 
Klaas van Aarsen said:
If it is a saddle point, then there must be a direction where $f$ goes to up, and also a direction where $f$ goes down.
Since $f$ goes to $+\infty$ in all directions, it's not going down. 🤔

I see! Thank you very much! 🤩
 

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