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$f(x,y)$ has partial derivatives in all $\mathbb R^2$ and a unique critical point at $(x_0,y_0)$ (local maximum). Is it a global maximum?

I know that in compact sets, it isn't enough to say that if a point is the only maximum inside the set, then it's a global maximum, because in the frontier of the set it could happen that the function has a maximum greater than the inside one. But for the entire $\mathbb R^2$ there is no frontier, therefore can I admit that this unique point is a point of global maximum?

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  • $\begingroup$ So you mean $(x_0,y_0)$ is the only critical point? $\endgroup$
    – zhw.
    Commented Oct 25, 2015 at 5:57
  • $\begingroup$ @zhw. yes thats it $\endgroup$ Commented Oct 25, 2015 at 5:58
  • $\begingroup$ Then please edit your question to say so, thanks. $\endgroup$
    – zhw.
    Commented Oct 25, 2015 at 5:58
  • $\begingroup$ $R^2$ is also not compact...... $\endgroup$ Commented Oct 25, 2015 at 7:08
  • $\begingroup$ There is a host of counter-examples. $\endgroup$ Commented Oct 25, 2015 at 7:20

1 Answer 1

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Loose description of the geometry: imagine a flat plane and then you put a lone hill with a peak on it. Now tilt the plane a little. Now you still have one peak, but hopefully you can also see that you have introduced a saddle point. Imagine sliding the saddle point location off to infinity to make it effectively no longer there.

Basic description of the construction I saw somewhere once: you can have something whose cross sections in one direction are cubic, but whose cross-sections in the other direction is a sum of exponentials in just the right way for one of the cubic cross-sectional peaks to meet the peak of one of the exponential sum cross-sections. This can happen with no other critical points, and of course the cubic component alone is unbounded in either direction (so there is no global max or min). The direction that the exponentials taper off in aligns with the idea of the saddle point being slid away to infinity.

Consider $f(x,y)=x^3+axe^{my}+be^{ny}$ for some constants $a,b,m,n$. We have: $$\begin{align} \frac{\partial}{\partial x}f(x,y)&=3x^2+ae^{my}&\frac{\partial}{\partial y}f(x,y)&=amxe^{my}+bne^{ny} \end{align}$$

To find a critical point, the $\frac{\partial}{\partial x}$ shows us $a$ must be negative. We are just looking for one example, so we pick some numbers for the constants that make the algebra that follows simpler. With $a=-3$, $b=1$, $m=1$, $n=3$:

$$\begin{align} \frac{\partial}{\partial x}f(x,y)&=3x^2-3e^{y}&\frac{\partial}{\partial y}f(x,y)&=-3xe^{y}+3e^{3y}\\ &=3(x^2-e^y)&&=3e^y(e^{2y}-x) \end{align}$$

So at a critical point, $x=e^{2y}$ according to the latter relation.

Then using the $\frac{\partial}{\partial x}$ relation: $$e^{4y}-e^y=0\implies e^{3y}=1\implies y=0$$ which in turn says that $x=1$. So the only critical point is at $(1,0)$. Is it a local min, max, or neither?

$$\begin{align} f_{xx}(1,0)&=6&f_{xy}(1,0)&=-3&f_{yy}=6 \end{align}$$

So the Hessian determinant is $6^2-(-3)^2=27>0$, so this critical point is either a local max or a local min. OK, it turns out to be a local min, since for example $f_{xx}(1,0)$ is positive. But $f$ has no global min, since you could fix $y$ and let $x\to-\infty$ and get unbounded large negative output.

Take this $f$ and negate it to get the direct counterexample. Here is an image of $-f$ from GeoGebra. Note how there would be a saddle point, except it has been slid off the map (to infinity) to the left.

enter image description here

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  • $\begingroup$ A very similar construction is discussed in this paper. $\endgroup$ Commented Jan 8, 2021 at 9:15
  • $\begingroup$ @RavenclawPrefect I think I saw this construction (or a similar one) some years ago while teaching a calculus course. It probably comes from the same place as that paper. $\endgroup$
    – 2'5 9'2
    Commented Jan 8, 2021 at 16:11
  • $\begingroup$ @alex.jordan If you don't mind, could you please take a look at my question here? Thank you so much. math.stackexchange.com/questions/3987955/… $\endgroup$
    – user56202
    Commented Jan 16, 2021 at 20:34

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