F doesn't have in (0,0) a local minimum

In summary, We have two functions, f and g, where g is a transformation of f with a variable t. We have shown that g has a local minimum at t=0, but we want to show that f does not have a local minimum at (0,0). We can find all critical points of f and determine their function values, which will show that (0,0) is a saddle point. We can also consider the curves g and h, which show that (0,0) is both a minimum and a maximum, making it a saddle point.
  • #1
mathmari
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Hey! :giggle:

We have the function $\displaystyle{f(x,y)=y^2-3x^2y+2x^4}$ and the function $\displaystyle{g_v(t)=f(tv_1, tv_2)=t^2v_2^2-3t^3v_1^2v_2+2t^4v_1^4}$.

I have shown that $g$ has a local minimumat $t=0$

I want to show that $f$ has not a local minimum in $(0,0)$.

The gradient is \begin{equation*}\nabla f=\begin{pmatrix}-6xy+8x^3 \\ 2y-3x^2\end{pmatrix}\end{equation*} Acritical point is $(0,0)$, since $\displaystyle{\nabla f\begin{pmatrix}0 \\ 0\end{pmatrix}=\begin{pmatrix}0 \\ 0\end{pmatrix}}$.
The Hessian matrix is \begin{equation*}H_f(x,y)=\begin{pmatrix}f_{xx} & f_{xy}\\ f_{yx} & f_{yy}\end{pmatrix}=\begin{pmatrix}-6y+24x^2 & -6x\\ -6x & 2\end{pmatrix}\end{equation*}
The matrixin in $(0,0)$ is
\begin{equation*}H_f(0,0)=\begin{pmatrix}0 & 0\\ 0 & 2\end{pmatrix}\end{equation*}
The eigenvalues are, $\lambda_1=0$, $\lambda_2=2>0$. These are non-negativ, so the Hessian matrix is positiv semidefinite.
That means that $f$ has in$(0,0)$ either a minimum or a saddle point.

Is that correct so far? Now we have to show that in $(0,0)$ has a sddle point and not a minimum, right? But how? :unsure:
 
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  • #2
Hey mathmari!

In this case it does not work to look at what $f$ does at infinity, since there is more than 1 critical point.
Can we find all critical points and find the function values at those critical points? 🤔
 
  • #3
Klaas van Aarsen said:
In this case it does not work to look at what $f$ does at infinity, since there is more than 1 critical point.
Can we find all critical points and find the function values at those critical points? 🤔

How does it help to check what kind of point we have at $(0,0)$ when we know all critical points? I got stuck right now. :unsure:
 
  • #4
Can we do also the following?

We consider the curves \begin{align*}&g(y)=f(0,y)=y^2\\ &h(x)=f(x,\frac{3}{2}x^2)= -\frac{x^4}{4}\end{align*}
Along the curve $g$ we go falling to a minimum at the origin but along the curve $h$ we go rising to a maximum at the origin.

This means that $(0,0)$ is a sattle point.

:unsure:
 
  • #5
Yep. That works. (Nod)
We've found a direction up and a direction down.

Turns out there are 2 critical points. The 2nd one has a negative value, which implies that there is a direction down. 🧐
 

1. What does it mean for F to not have a local minimum at (0,0)?

When a function F does not have a local minimum at a specific point, it means that there is no point in the immediate vicinity of (0,0) where F has a lower value. In other words, (0,0) is not a point of lowest value for F within a small region around it.

2. Why is it important for a function to have a local minimum at (0,0)?

A local minimum at (0,0) is important because it indicates the lowest value of the function within a small region around (0,0). This can be useful in optimization problems, where finding the minimum value of a function is important.

3. How can you determine if a function has a local minimum at (0,0)?

To determine if a function has a local minimum at (0,0), you can use the first and second derivative tests. If the first derivative is zero at (0,0) and the second derivative is positive, then (0,0) is a local minimum. If the second derivative is negative, then (0,0) is a local maximum.

4. Can a function have multiple local minima at (0,0)?

Yes, a function can have multiple local minima at (0,0) if the first derivative is zero at multiple points and the second derivative is positive at those points. In this case, (0,0) would be considered a critical point rather than a local minimum.

5. What is the difference between a local minimum and a global minimum?

A local minimum is the lowest value of a function within a small region around a specific point, while a global minimum is the lowest value of the function over its entire domain. A global minimum may or may not coincide with a local minimum at a specific point.

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