Second Partial Derivatives of Implicit Functions

In summary, the conversation discusses difficulties with calculating second partial derivatives of implicit functions in Calculus. The speakers mention that Kaplan and Spiegel only briefly cover this topic in their advanced calculus books and that simply repeating the methods used for first derivatives does not seem to work. They also mention using the method of Jacobians to find the first derivative and how the second derivative can be found by chaining the differential operator. The conversation ends with a clarification on which equations are used to find the second derivative.
  • #1
rick1138
196
0
I have been reviewing Calculus and have tripped up on figuring out to calculate the 2nd partial derivatives of imlicit functions. Kaplan and Spiegel give a cursory treatment to the subject in both of their "Advanced Calculus" books. Simply repeating the methods used to calculate the 1st derivatives doesn't appear to work. Any information would be appreciated.
 
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  • #2
Simply repeating the methods used to calculate the 1st derivatives doesn't appear to work. Any information would be appreciated.

Why don't they appear to work?
 
  • #3
I don't know - perhaps because the equations are implicitly defined. I figured out how the answers were calculated, I can apply the same formula and get the correct answer, but I don't know why it is correct - the "naive" solution appears to work correctly, at least integrating the 2nd derivatives come about by using the "naive" solution produces the first. I am going to post in more detail later.
 
  • #4
Ok here it is. This example is from Kaplan's book - the detail is provided by me. Given a system of two functions:

[tex]
F \equiv ( x^2 + y^2 + u^2 + v^2 - 1)
[/tex]
[tex]
G \equiv ( x^2 + 2y^2 - u^2 + v^2 - 1)
[/tex]

Where u and v depend on x and y:

[tex]
u = f(x,y)
[/tex]
[tex]
v = g(x,y)
[/tex]

find

[tex]
\frac{\partial^2 x}{\partial u^2}
[/tex]

To find the first derivative, the method of Jacobians can be used. I am not going to provide the details, but the result is:

[tex]
\frac{\partial x}{\partial u} = - \frac{3u}{x}[/tex]

Finding the second derivative depends on the fact that the differential operator can be chained:

[tex]\frac{\partial}{\partial u} = \frac{\partial}{\partial x} \frac{\partial x}{\partial u}[/tex]

So

[tex]\frac{\partial^2 x}{\partial u^2} = \frac{\partial}{\partial u} \frac{\partial x}{\partial u} = \frac{\partial}{\partial u} (- \frac{3u}{x})[/tex]

Considering the function as a product of two functions and applying the product rule for derivatives produces:

[tex]\frac{\partial}{\partial u} [(- 3u)(\frac{1}{x})] = [\frac{\partial}{\partial u} (- 3u)](\frac{1}{x}) - 3u[\frac{\partial}{\partial u} (\frac{1}{x})] [/tex]

After some manipulation:

[tex]- \frac{3}{x} - 3u[\frac{\partial x}{\partial u}(\frac{\partial}{\partial x}\frac{1}{x})] = - \frac{3}{x} - 3u(- \frac{3u}{x})(- \frac{1}{x^2})[/tex]

And finally, the answer:

[tex]- \frac{3}{x} - \frac{9y^2}{x^3}[/tex]
 
Last edited:
  • #5
Are u using only the first equation (the one for F), or are u using the second (for G) as well?


Daniel.
 
Last edited:
  • #6
Both F and G - they are used in the Jacobian to produce

[tex]\frac{\partial x}{\partial u} = - \frac{3u}{x}[/tex].
 
  • #7
rick1138 said:
Finding the second derivative depends on the fact that the differential operator can be chained:

[tex]\frac{\partial}{\partial u} = \frac{\partial}{\partial x} \frac{\partial x}{\partial u}[/tex]

It's actually in reversed order

[tex] \frac{\partial}{\partial u} = \frac{\partial x}{\partial u} \frac{\partial}{\partial x}[/tex]

Daniel.
 

What is a second partial derivative of an implicit function?

A second partial derivative of an implicit function is the rate of change of the slope of the tangent line at a particular point on the function's graph, with respect to two different variables.

How is a second partial derivative calculated?

A second partial derivative is calculated by taking the derivative of the first partial derivative of the function, with respect to the second variable.

What does a second partial derivative tell us about the function?

A second partial derivative can tell us about the curvature of the function at a specific point, and whether the function is concave up or concave down at that point.

Can a second partial derivative be negative?

Yes, a second partial derivative can be negative, indicating that the function is concave down at a particular point.

How are second partial derivatives used in real-world applications?

Second partial derivatives are used in many fields of science and engineering, such as physics, economics, and computer science. They can help in analyzing the behavior of complex systems and making predictions about future outcomes.

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