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

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SUMMARY

The discussion centers on finding points on the surface defined by the equation \( z^2 = 8 + (x-y)^2 \) that minimize the distance to the point (3,0,0). The function \( f(x,y) = (x-3)^2 + y^2 + (x-y)^2 \) has a minimum at the critical point (2,1), confirmed by calculating the gradient and Hessian matrix. The conclusion is that the minimum distance to the point (3,0,0) occurs at the same point as the minimum of \( f \), as both functions increase to infinity in all directions, ruling out saddle points.

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  • Understanding of gradient and Hessian matrix in multivariable calculus
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  • Knowledge of critical points and their classification
  • Basic concepts of optimization in mathematical functions
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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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