Hi, TOPGMAT!
I guess here is not the proper place to deal with Calculus of functions of two-real variables but... let´s talk a bit about it, no problem. (Thank you for the "thanks", by the way.)
Let us consider that f=f(x,y) is defined for all the plane RxR, as it is the case with the explicit f=f(x,y) function you chose. (Good one for the discussions, let´s focus on it!!)
When we consider (say) y fixed [x fixed] and we differentiate f related to the x variable [y variable] , we are calculating the partial x-derivative [y-derivative] of f. Let us called them D1f (for x) and D2f (for y), ok?
In your example, D1f(x,y) = y^2 + 2xy and D2f(x,y) = x^2 + 2xy. (Hope you remember this calculations.)
From the fact that D1f and D2f are continuous functions in every (x,y) point of RxR, there is a theorem that assures us that f = f(x,y) is differentiable everywhere, that is, in the whole RxR plane. From the fact that the partial derivatives of D1f and D2f (viewed simply as functions of x and y themselves) are also continuous in the plane, and so on, we are sure the function you chose is (called C^infinity and)is really great in terms of "good behavior" and in terms of being able to calculate D1(D1f) we will call D11f, D2(D2f) we will call D22f and the "mixed" ones, D1(D2f) and D2(D1f), not to mention the next ones D111f, etc (and infinitum)...
Besides that, whenever we have D1f(a,b) = D2f(a,b) = 0, we say (a,b) is a critical point of f and it is known that a NECESSARY condition for f to have a local extremum at (a,b) is that (a,b) is a critical point of f.
The problem is that this is not SUFFICIENT, in other words, we have to consider the second derivatives (I mean D11f, D12f, etc) to determine whether we have a local maximum, a local minimum or yet a (so called) saddle point.As the name suggests, in a neighborhood of a saddle point you have points where the function gets smaller and greater values than in itself and therefore, no minimum or maximum is attained in saddle points.
A typical test to decide that (minimum/maximum/saddle) is the so-called "second-derivative" test and this test is related to the "hessian matrix" (google about it, if you want additional info). This is the matrix I had in mind in my previous post, by the way...
In the function you chose, from the fact that D11f(0,0)*D22f(0,0) - D12f(0,0) = 0, the test gives no conclusion (we say the test is "inconclusive"), but it is not hard to guess that (0,0) is a saddle point, because if you take (say) (x,y)=(0.001, 0.001) it is easy to see that f(x,y) is greater than 0 (therefore, in a non-rigourous way, you see that the origin is not a local maximum because you could use 0.0001 and 0.0001... got it?) and if you take (x,y) = (-0.001, -0.001) then f(x,y) is less than 0, and now in a very non-rigorous way you see that the origin is also not a local minimum (same idea)... therefore the origin is a saddle point.
Your example is, therefore, one good counter-example to people who believe that "(partial) differentiate and equal to zero" will get (always) a maximum or minimum point for the function in question, but it is true that, when this operations are viable (when f is at least twice differentiable it´s the case), the critical points you may obtain are the ONLY "candidates" for maximum/minimum, for sure! (So you could "test" one by one, by some methods as the hessian analysis, or even as stupid as mine, trying to calculate values "near" the critical point considered.)
In summary: the single (real) variable scenario is much easily "tamed" than the more-than-one variable scenario, therefore I guess my suggestion should be taken seriously: avoid using Calculus on the GMAT because it is certainly not necessarily and, sometimes, may lead you to some subtle details that, in general, the candidate will not remember nor have enough time to deal with during the question, not to mention some wrong conclusions he/she might take.
I hope you got the point and please apology any lack of rigorous on my part: now is saturday night here in Brazil, and my wife is asking me to see a DVD with her for a couple of minutes already...
Regards,
Fabio.