Critical Points in the First Quadrant

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Homework Help Overview

The discussion revolves around finding critical points of a function defined in the first quadrant, specifically focusing on the volume expressed as xyz = xy(1 - x² - y²). Participants are exploring the conditions under which critical points occur and the implications of second derivatives in determining the nature of these points.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning, Problem interpretation

Approaches and Questions Raised

  • Participants discuss setting the first derivatives to zero to find critical points and express confusion about how to isolate these points in the first quadrant. There are attempts to manipulate the equations derived from the first derivatives to find solutions. Questions arise regarding the reasoning behind using second derivatives to classify the critical points and the implications of the results obtained.

Discussion Status

The discussion is ongoing, with participants sharing their attempts and expressing confusion about certain aspects of the problem. Some have provided partial results and insights into the classification of critical points, while others are still grappling with the underlying concepts.

Contextual Notes

There is mention of constraints related to the first quadrant (x > 0, y > 0) and the need to understand the implications of second derivatives without having reached a consensus on the classification of critical points.

jegues
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Homework Statement


See figure.


Homework Equations


N/A.


The Attempt at a Solution



Part A:

The volume is,

[tex]xyz = xy(1 - x^{2} - y^{2})[/tex]

Critical points:
[tex]f_{x} = y-3x^{2}y-y^{3} = 0[/tex]

[tex]f_{y} = x -x^{3} - 3xy^{2} = 0[/tex]

Part B:

This is where I get confused, how do I find the critical point in the first quadrant assuming x>0 and y>0 ?

NOTE: I haven't gotten to parts C and D yet, I'll update this thread as I go.
 

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jegues said:
Critical points:
[tex]f_{x} = y-3x^{2}y-y^{3} = 0[/tex]

[tex]f_{y} = x -x^{3} - 3xy^{2} = 0[/tex]

Part B:

This is where I get confused, how do I find the critical point in the first quadrant assuming x>0 and y>0 ?

Set [tex]f_x = f_y = 0.[/tex] Since x and y are not zero here, we can divide f_x by y and f_y by x, giving us:

[tex]f_{x} = 0 = 1 - 3x^2 - y^2 = 0[/tex]

[tex]f_{y} = 0 = 1 - x^2 - 3y^2 = 0[/tex]

You should be able to find an (x,y) that satisfies these equations easily. [STRIKE]In reality, there are an infinite number of such points that would work.[/STRIKE] Ignore that last bit, idk what I was thinking xD...
 
Last edited:
[tex]x^{2}=y^{2}= \frac{1}{4}[/tex]

Therefore,

[tex](x,y) = (\frac{1}{2},\frac{1}{2})[/tex]

I'll keep updating this thread as I progress through the other parts.

Thanks again!
 
Yep, that works!
 
Working on Part C:

I'm a little confused with this part. I took a peak at the solutions and I see the following,

[tex]f_{xx} = -6xy = \frac{-3}{2}[/tex]

[tex]f_{yy} = -6xy = \frac{-3}{2}[/tex]

[tex]f_{xy} = 1 - 3x^{2} - 3y^{2} = \frac{-1}{2}[/tex]

The mechanics of all this (i.e. taking the partial of fx wrt x again) makes sense and so do the results. The part I don't understand is WHY we are doing this to obtain information about the nature of the critical point. (in other words how is this helping us?)

Also they throw in this line at the end,

So,

[tex]f_{xx}f_{yy}-f_{xy}^{2} > 0,[/tex] and [tex]f_{xx} < 0[/tex]

Therefore it is a local maximum.

I'm confused as to how he drew this conclusion? How do all these partial derivatives, and specifically that line above, tell us whether it is a max or a min?

Thanks in advance!
 
Let M = [tex]f_{xx}f_{yy}-f_{xy}^{2}.[/tex]

If M > 0, then [tex]f_{xx}f_{yy} > f_{xy}^{2}.[/tex] So f_xx and f_yy must have the same sign (positive or negative) and are sufficiently large enough to offset f_xy (this is a bit more complicated, ignore for now).
If positive, then both the x and y cross sections are concave up, i.e., local min.
Similarly, if negative, then concave down, i.e., local max.

If M < 0 and f_xx and f_yy have different signs, then the x and y cross sections have opposite concavity. Thus, we have a saddle point.

If M < 0 and f_xx and f_yy have the same sign, then things are a tiny bit more complicated. I'm too lazy to type it all out, but the result is that the critical point is a saddle point here.

By the way, all this should be on wikipedia, wolframalpha, etc. Just search "second derivative test in multivariable calculus" in Google.
 
Oh, and if M = 0, our test fails :'(!
 

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