Finding local min, max, and saddle points in multivariable calculus

In summary, the conversation discusses finding the local maximum and minimum values and saddle points of a given function using the Second Derivative Test. However, the test is inconclusive and the textbook provides a different way to find the local maximum value of 1 at all points of the form (x, x). A suggestion is made to rewrite the function and use factored form to find the global maxima and minima.
  • #1
woodenbox
7
0

Homework Statement



Find the local maximum and minimum values and saddle point(s) of the function.

f(x,y) = 1 + 2xy - x^2 - y^2

Homework Equations



The Second Derivative Test: let D = D(a,b) = fxx(a,b)*fyy(a,b) - [fxy(a,b)]^2
if D > 0 and fxx(a,b) > 0, then f(a,b) is a local minimum
if D > 0 and fxx(a,b) < 0, then f(a,b) is a local maximum
if D < 0 then f(a,b) is a saddle point
if D = 0 then the test is inconclusive

The Attempt at a Solution



I tried to use the Second Derivative Test to find the local mins, maxes, and saddle points but it's inconclusive, and I don't know how else to find them. My textbook says the answer is "f has a local maximum value of 1 at all points of the form (x, x)"

This is my work for the Second Derivative Test:

fx = 2y - 2x = 0 --> 2y = 2x --> y = x

fy = 2x - 2y = 0 --> 2x - 2(x) = 0 --> 0 = 0

so i guess there are critical points at every value where y = x... which matches the textbook's answer.
and then:

fxx = -2

fyy = -2

fxy = 2

so D = fxx * fyy - (fxy)^2 = (-2)*(-2) - 2^2 = 4 - 4 = 0 so the test is inconclusive

Is there a different way to find the local mins, maxes, and saddle points?
 
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  • #2
got the answer, for anyone else who looks this up.

Rewriting the function as f(x, y) = (x - y)^2 + 1,
we see that the minimum value must be 1 (since 0 is the smallest value of a square),
and this is attained whenever y = x (i.e., points of the form (x, x)).
 
  • #3
woodenbox said:

Homework Statement



Find the local maximum and minimum values and saddle point(s) of the function.

f(x,y) = 1 + 2xy - x^2 - y^2

Homework Equations



The Second Derivative Test: let D = D(a,b) = fxx(a,b)*fyy(a,b) - [fxy(a,b)]^2
if D > 0 and fxx(a,b) > 0, then f(a,b) is a local minimum
if D > 0 and fxx(a,b) < 0, then f(a,b) is a local maximum
if D < 0 then f(a,b) is a saddle point
if D = 0 then the test is inconclusive

The Attempt at a Solution



I tried to use the Second Derivative Test to find the local mins, maxes, and saddle points but it's inconclusive, and I don't know how else to find them. My textbook says the answer is "f has a local maximum value of 1 at all points of the form (x, x)"

This is my work for the Second Derivative Test:

fx = 2y - 2x = 0 --> 2y = 2x --> y = x

fy = 2x - 2y = 0 --> 2x - 2(x) = 0 --> 0 = 0
fx = 2y - 2x
fx = 0 ==> y = x

fy = 2x - 2y
fy = 0 ==> x = y

fx and fy are both zero along the line y = x.
woodenbox said:
so i guess there are critical points at every value where y = x... which matches the textbook's answer.
and then:

fxx = -2

fyy = -2

fxy = 2

so D = fxx * fyy - (fxy)^2 = (-2)*(-2) - 2^2 = 4 - 4 = 0 so the test is inconclusive

Is there a different way to find the local mins, maxes, and saddle points?

The function can be written as f(x, y) = 1 - (x2 - 2xy + y2), and the right side can be written in factored form, which should give you some ideas for finding the global maxima and minima.
 
  • #4
that does make a lot of sense... i never thought to take a more direct approach. thank you for your help!
 

1. What is a local min, max, and saddle point in multivariable calculus?

A local min, max, and saddle point are all critical points in a multivariable function. A local minimum is a point where the function is at its lowest value in a small region around that point. A local maximum is a point where the function is at its highest value in a small region around that point. A saddle point is a point where the function has both increasing and decreasing slopes in different directions.

2. How do you find local min, max, and saddle points in multivariable calculus?

To find local min, max, and saddle points, you need to first take the partial derivatives of the function with respect to each variable. Then, set each partial derivative equal to 0 and solve for the variables. Once you have the values of the variables, you can plug them back into the original function to find the corresponding values of the function at those points. The points where the partial derivatives are equal to 0 are the critical points, which may be local min, max, or saddle points.

3. Can a function have multiple local min, max, and saddle points?

Yes, a function can have multiple local min, max, and saddle points. In fact, a function can have an infinite number of local min, max, and saddle points.

4. How can you determine if a critical point is a local min, max, or saddle point?

To determine if a critical point is a local min, max, or saddle point, you can use the second derivative test. Take the second partial derivatives of the function and plug in the values of the variables at the critical point. If the second derivative is positive, the critical point is a local minimum. If the second derivative is negative, the critical point is a local maximum. If the second derivative is 0, further analysis is needed to determine if the critical point is a saddle point.

5. Is it possible for a function to have no local min, max, or saddle points?

Yes, it is possible for a function to have no local min, max, or saddle points. This can happen if the function is constant or has no critical points. In this case, the entire function would be either a global minimum, maximum, or neither.

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