Understanding Stationary Points: Saddle, Max, or Min?

[ibysaiyan]

Homework Statement

Hi,

I have been given a set of functions for which I need to find the stationary points , and determine whether the points are saddle, or max/min.
I think I may have solved it correctly but I end up with all the points being saddle, surely this can't be right.. I may have gone wrong with my arithmetic. Can anyone go through my working. Appreciate the replies.

Homework Equations

The Attempt at a Solution

f(x,y) = x^4 +(2x^2)y -4x^2 +3y^2

F_{x} = 4x^3 +4xy-8x
F_{y} =2x^2+6y
F_{xx}12x^2 +4y -8
F_{yy}6
F_{yx}4x

The definition which I have used for delta/ determinant is : If Δ > 0 then stationary points are saddle i.e f_{xy}^2 - f_{xx} * f_{yy}

The points which I get are the following:
Re arranging (eq.1) and using eq. 2 (2x^2 = -6y)
4x^3 +4xy-8x (eq.1)
=>
x(4x^2 +4y-8) = 0
x[ (2x^2 +2x^2 ]+4y-8 = 0
x[ (-6y-6y) +4y -8] =0
x(-12+4y-8) = 0

x = 0 , -8y = 8 , y=-1
Points are:
(0,0) , (+/√3, -1)

Thanks!

 

[HallsofIvy]

Homework Statement

Hi,

I have been given a set of functions for which I need to find the stationary points , and determine whether the points are saddle, or max/min.
I think I may have solved it correctly but I end up with all the points being saddle, surely this can't be right.. I may have gone wrong with my arithmetic. Can anyone go through my working. Appreciate the replies.

Homework Equations

The Attempt at a Solution

f(x,y) = x^4 +(2x^2)y -4x^2 +3y^2

F_{x} = 4x^3 +4xy-8x
F_{y} =2x^2+6y
F_{xx}12x^2 +4y -8
F_{yy}6
F_{yx}4x

The definition which I have used for delta/ determinant is : If Δ > 0 then stationary points are saddle i.e f_{xy}^2 - f_{xx} * f_{yy}

You have this exactly backwards- the stationary points are saddles if Δ< 0.
The point is that f_{xy}^2- f_{xx}f_{yy} is the determinant of the "second derivative matrix"
\begin{bmatrix}f_{xx}&amp; f_{xy} \\ f_{xy} &amp; f_{yy}\end{bmatrix}
of course that determinant is independent of the coordinate system- and since this is a symmetric matrix, there exist a coordinate system in which it is diagonal. That is there exist a coordinate system in which the matrix is
\begin{bmatrix}f_{xx} &amp; 0 \\ 0 &amp; f_{yy}\end{bmatrix}
which means that, locally, it is like f_{xx}x^2+ f_{yy}y^2. If the determinant is negative, one of those coefficients is positive, the other negative, a saddle point. If the determinant is positive, the coefficients are either both positive, a local minimum, or both negative, a local maximum.

The points which I get are the following:
Re arranging (eq.1) and using eq. 2 (2x^2 = -6y)
4x^3 +4xy-8x (eq.1)
=>
x(4x^2 +4y-8) = 0
x[ (2x^2 +2x^2 ]+4y-8 = 0
x[ (-6y-6y) +4y -8] =0
x(-12+4y-8) = 0

x = 0 , -8y = 8 , y=-1
Points are:
(0,0) , (+/√3, -1)

Thanks!

 

[ibysaiyan]

You have this exactly backwards- the stationary points are saddles if Δ< 0.
The point is that f_{xy}^2- f_{xx}f_{yy} is the determinant of the "second derivative matrix"
\begin{bmatrix}f_{xx}&amp; f_{xy} \\ f_{xy} &amp; f_{yy}\end{bmatrix}
of course that determinant is independent of the coordinate system- and since this is a symmetric matrix, there exist a coordinate system in which it is diagonal. That is there exist a coordinate system in which the matrix is
\begin{bmatrix}f_{xx} &amp; 0 \\ 0 &amp; f_{yy}\end{bmatrix}
which means that, locally, it is like f_{xx}x^2+ f_{yy}y^2. If the determinant is negative, one of those coefficients is positive, the other negative, a saddle point. If the determinant is positive, the coefficients are either both positive, a local minimum, or both negative, a local maximum.

Hi there,
You see on my lecturer's note I can clearly see that he has stated that when a coordinate system has Δ >0 then it's a saddle point , he did mention that " some books use the expression the other way around" , which I have confirmed upon browsing.

Could he be wrong? Also thanks for your in-depth answer ( as always).