I want to know the maximum and minimum absolutes values of this function:
$\ f(x,y)= 4x^2 + 9y^2 - x^2y^2 $
$\nabla f(x,y)=(8x-2xy^2,18y-2yx^2) $
I find these critical points:
$\ (0,0);(3,2);(-3,2);(3,-2);(-3;-2) $
In$\ (0,0)$ the Hessian critirium work,$\ (0,0)$ its a local minimum
But wait:
for $\ (3,2)$: $$ \begin{bmatrix} 0 & -24 \\ -24 & 0 \\ \end{bmatrix} $$
So in this case (and in the other points) the Hessian criterium doesn't work.
How I can demonstrate than $\ (0,0)$ es a absolute minimum of the function, and what about the other points? Are they saddle points? Max, Min?
Thanks very much, I'm sorry for my bad English.
Here in Argentina, the university is free, but English lessons not.


Hint: Test for Extrema
Let $f$ be a function of two variables that has continuous second partial derivatives on a rectangular region $Q$ and let:
$$\displaystyle g(x,y) = f_{xx}(x, y)f_{yy}(x,y)-[f_{xy}(x,y)]^2$$
for every $(x,y)$ in $Q$.
If $(a,b)$ is in $Q$ and $f_x(a,b) = 0, f_y(a,b) = 0$, then:
We are given:
$$f(x,y)= 4x^2 + 9y^2 - x^2y^2$$
The critical points (you did great) are:
$$(0,0);(3,2);(-3,2);(3,-2);(-3,-2)$$
We have:
Now evaluate each of the critical points using the above definition.
Also, it helps to sometimes plot the function and we have:
Examples:
Update:
The Hessian determinant is given by:
$$\det(H) = \begin{vmatrix} f_{xx} & f_{xy} \\ f_{yx} & f_{yy}\end{vmatrix} \ $$
If you are using the Hessian, there are four conditions you need to test:
So, for each critical point that you found, can you now classify it using the criteria above and your approach?
Can you compare this to the approach above?
Can you look at other criteria to help distinguish the critical points as alluded to in the comments?