Analysis of Implicit Differentiation

In summary: Now Dh (x,y) = Dg (u_1, u_2) D \underline{u} (x,y) That is \begin {bmatrix} \frac{ \partial h }{ \partial x} & \frac{ \partial h }{ \partial y} \end{bmatrix} = \begin {bmatrix} \frac{ \partial g }{ \partial u_1} & \frac{ \partial gh }{ \partial u_2} \end{bmatrix
  • #1
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I am reading several books on multivariable analysis/calculus and am trying to get a precise and rigorous theoretical understanding of implicit differentiation, including the Implicit Function Theorem ... in particular I am reading:

Vector Calculus (Second Edition) by Susan Colley

and

Calculus: Volume II (Second Edition) by Tom Apostol

I am trying to carefully relate the working in Apostol's first worked example in Section 9.7, just following Section 9.7 on the derivative of a function defined implicitly ... ... Worked Example 1 reads as follows:
View attachment 4041As indicated above, I have tried to work through this example carefully relating it to Colley's definition of the Chain Rule and also trying to be explicit about the structure of the functions involved ... Colley's statement of the Chain Rule is as follows:
View attachment 4042
https://www.physicsforums.com/attachments/4043
I have also tried to follow (very carefully) Apostol's analysis in Section 9.6 which reads as follows:

https://www.physicsforums.com/attachments/4044
View attachment 4045

I will now provide my analysis and would very much appreciate someone critiquing my analysis and point out any shortcomings, misinterpretations or errors ...We have \(\displaystyle g(x,y) = 0\) and \(\displaystyle y = Y(x)\) for all \(\displaystyle x\) in some open interval \(\displaystyle (a,b)\) ... and we let

\(\displaystyle G(x) = g[ x, Y(x)]\) for \(\displaystyle x \in (a,b)\) ... ...

Then ...

Following Apostol, Section 9.6 we put

\(\displaystyle u_1(x,y) = x\)

and

\(\displaystyle u_2(x,y) = y = Y(x)\)Then let \(\displaystyle G(x) = g[ u_1(x,y), u_2(x,y) ]\)

Let \(\displaystyle \underline{u} (x,y) = ( u_1, u_2)\)

So we have the situation as shown below in Figure 1.
View attachment 4046

So continuing ... for \(\displaystyle x \in (a,b)\) we have\(\displaystyle D(g \circ \underline{u} ) (x,y) = Dg (u_1, u_2) D \underline{u} (x,y) \)

or

... if we let \(\displaystyle h = g \circ \underline{u}\) then we have:

\(\displaystyle Dh (x,y) = Dg (u_1, u_2) D \underline{u} (x,y) \)

... ...Now we have that ...\(\displaystyle Dh (x,y) = \begin {bmatrix} \frac{ \partial h }{ \partial x} & \frac{ \partial h }{ \partial y} \end{bmatrix}
\)\(\displaystyle Dg (u_1, u_2) = \begin {bmatrix} \frac{ \partial g }{ \partial u_1} & \frac{ \partial gh }{ \partial u_2} \end{bmatrix}\)

and

\(\displaystyle D \underline{u} (x,y) = \begin {bmatrix} \frac{ \partial u_1 }{ \partial x} & \frac{ \partial u_1 }{ \partial y} \\ \frac{\partial u_2 }{ \partial x} & \frac{ \partial u_2 }{ \partial y} \end{bmatrix}\)Now

\(\displaystyle Dh (x,y) = Dg (u_1, u_2) D \underline{u} (x,y) \)That is\(\displaystyle \begin {bmatrix} \frac{ \partial h }{ \partial x} & \frac{ \partial h }{ \partial y} \end{bmatrix} = \begin {bmatrix} \frac{ \partial g }{ \partial u_1} & \frac{ \partial gh }{ \partial u_2} \end{bmatrix} \begin {bmatrix} \frac{ \partial u_1 }{ \partial x} & \frac{ \partial u_1 }{ \partial y} \\ \frac{\partial u_2 }{ \partial x} & \frac{ \partial u_2 }{ \partial y} \end{bmatrix}\) ... ... ... (1)

So then (1) gives\(\displaystyle \frac{ \partial h }{ \partial x} = \frac{ \partial g }{ \partial u_1} \frac{ \partial u_1 }{ \partial x} + \frac{ \partial g }{ \partial u_2} \frac{ \partial u_2 }{ \partial x}\)But ... ... since \(\displaystyle u_1(x,y) = x\) we can view \(\displaystyle \frac{ \partial g }{ \partial u_1}\) as \(\displaystyle \frac{ \partial g }{ \partial x}\)and we also have \(\displaystyle \frac{ \partial u_1 }{ \partial x} = 1 \)Further ...

... since \(\displaystyle u_2(x,y) = y = Y(x)\) we can write \(\displaystyle \frac{ \partial g }{ \partial u_2}\) as \(\displaystyle \frac{ \partial g }{ \partial y}\)and we also have \(\displaystyle \frac{ \partial u_2 }{ \partial x} = \frac{ \partial Y(x) }{ \partial x} = \frac{dY}{dx} = Y'(x)\) since \(\displaystyle Y\) is a function of \(\displaystyle x\) ...so

\(\displaystyle \frac{ \partial h }{ \partial x} = \frac{ \partial g }{ \partial x} \cdot 1 + \frac{ \partial g }{ \partial y} \cdot Y'(x)\) But ...

\(\displaystyle \frac{ \partial h }{ \partial x} = \frac{ \partial g[ u_1(x,y), u_2(x,y) ] }{ \partial x} = G'(x)\)

so we can write \(\displaystyle G'(x) = \frac{ \partial g }{ \partial x} \cdot 1 + \frac{ \partial g }{ \partial y} \cdot Y'(x) \)

and so we have

\(\displaystyle Y'(x) = - \ \frac{ \frac{ \partial g }{ \partial x} }{ \frac{ \partial g }{ \partial y} }\)I would be most grateful if someone could critique my analysis above and point out any shortcomings, misinterpretations or errors ... or just confirm the above analysis is OK ...

Peter
 
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  • #2
Peter said:
I am reading several books on multivariable analysis/calculus and am trying to get a precise and rigorous theoretical understanding of implicit differentiation, including the Implicit Function Theorem ... in particular I am reading:

Vector Calculus (Second Edition) by Susan Colley

and

Calculus: Volume II (Second Edition) by Tom Apostol

I am trying to carefully relate the working in Apostol's first worked example in Section 9.7, just following Section 9.7 on the derivative of a function defined implicitly ... ... Worked Example 1 reads as follows:
As indicated above, I have tried to work through this example carefully relating it to Colley's definition of the Chain Rule and also trying to be explicit about the structure of the functions involved ... Colley's statement of the Chain Rule is as follows:I have also tried to follow (very carefully) Apostol's analysis in Section 9.6 which reads as follows:

I will now provide my analysis and would very much appreciate someone critiquing my analysis and point out any shortcomings, misinterpretations or errors ...We have \(\displaystyle g(x,y) = 0\) and \(\displaystyle y = Y(x)\) for all \(\displaystyle x\) in some open interval \(\displaystyle (a,b)\) ... and we let

\(\displaystyle G(x) = g[ x, Y(x)]\) for \(\displaystyle x \in (a,b)\) ... ...

Then ...

Following Apostol, Section 9.6 we put

\(\displaystyle u_1(x,y) = x\)

and

\(\displaystyle u_2(x,y) = y = Y(x)\)Then let \(\displaystyle G(x) = g[ u_1(x,y), u_2(x,y) ]\)

Let \(\displaystyle \underline{u} (x,y) = ( u_1, u_2)\)

So we have the situation as shown below in Figure 1.So continuing ... for \(\displaystyle x \in (a,b)\) we have\(\displaystyle D(g \circ \underline{u} ) (x,y) = Dg (u_1, u_2) D \underline{u} (x,y) \)

or

... if we let \(\displaystyle h = g \circ \underline{u}\) then we have:

\(\displaystyle Dh (x,y) = Dg (u_1, u_2) D \underline{u} (x,y) \)

... ...Now we have that ...\(\displaystyle Dh (x,y) = \begin {bmatrix} \frac{ \partial h }{ \partial x} & \frac{ \partial h }{ \partial y} \end{bmatrix}
\)\(\displaystyle Dg (u_1, u_2) = \begin {bmatrix} \frac{ \partial g }{ \partial u_1} & \frac{ \partial gh }{ \partial u_2} \end{bmatrix}\)

and

\(\displaystyle D \underline{u} (x,y) = \begin {bmatrix} \frac{ \partial u_1 }{ \partial x} & \frac{ \partial u_1 }{ \partial y} \\ \frac{\partial u_2 }{ \partial x} & \frac{ \partial u_2 }{ \partial y} \end{bmatrix}\)Now

\(\displaystyle Dh (x,y) = Dg (u_1, u_2) D \underline{u} (x,y) \)That is\(\displaystyle \begin {bmatrix} \frac{ \partial h }{ \partial x} & \frac{ \partial h }{ \partial y} \end{bmatrix} = \begin {bmatrix} \frac{ \partial g }{ \partial u_1} & \frac{ \partial gh }{ \partial u_2} \end{bmatrix} \begin {bmatrix} \frac{ \partial u_1 }{ \partial x} & \frac{ \partial u_1 }{ \partial y} \\ \frac{\partial u_2 }{ \partial x} & \frac{ \partial u_2 }{ \partial y} \end{bmatrix}\) ... ... ... (1)

So then (1) gives\(\displaystyle \frac{ \partial h }{ \partial x} = \frac{ \partial g }{ \partial u_1} \frac{ \partial u_1 }{ \partial x} + \frac{ \partial g }{ \partial u_2} \frac{ \partial u_2 }{ \partial x}\)But ... ... since \(\displaystyle u_1(x,y) = x\) we can view \(\displaystyle \frac{ \partial g }{ \partial u_1}\) as \(\displaystyle \frac{ \partial g }{ \partial x}\)and we also have \(\displaystyle \frac{ \partial u_1 }{ \partial x} = 1 \)Further ...

... since \(\displaystyle u_2(x,y) = y = Y(x)\) we can write \(\displaystyle \frac{ \partial g }{ \partial u_2}\) as \(\displaystyle \frac{ \partial g }{ \partial y}\)and we also have \(\displaystyle \frac{ \partial u_2 }{ \partial x} = \frac{ \partial Y(x) }{ \partial x} = \frac{dY}{dx} = Y'(x)\) since \(\displaystyle Y\) is a function of \(\displaystyle x\) ...so

\(\displaystyle \frac{ \partial h }{ \partial x} = \frac{ \partial g }{ \partial x} \cdot 1 + \frac{ \partial g }{ \partial y} \cdot Y'(x)\) But ...

\(\displaystyle \frac{ \partial h }{ \partial x} = \frac{ \partial g[ u_1(x,y), u_2(x,y) ] }{ \partial x} = G'(x)\)

so we can write \(\displaystyle G'(x) = \frac{ \partial g }{ \partial x} \cdot 1 + \frac{ \partial g }{ \partial y} \cdot Y'(x) \)

and so we have

\(\displaystyle Y'(x) = - \ \frac{ \frac{ \partial g }{ \partial x} }{ \frac{ \partial g }{ \partial y} }\)I would be most grateful if someone could critique my analysis above and point out any shortcomings, misinterpretations or errors ... or just confirm the above analysis is OK ...

Peter
Hello Peter,

I would like to help you but your post is too long to read.

I suggest you do the following.

Write an expository post on your understanding of the implicit differentiation using references from textbooks as little as possible.

Thus, each notation you use will need to be defined within your post, except, of course, the standard ones.

Then I think it would be more amenable to people.

If you choose to do so, drop me a visitor message.

I will try to help.
 
  • #3
caffeinemachine said:
Hello Peter,

I would like to help you but your post is too long to read.

I suggest you do the following.

Write an expository post on your understanding of the implicit differentiation using references from textbooks as little as possible.

Thus, each notation you use will need to be defined within your post, except, of course, the standard ones.

Then I think it would be more amenable to people.

If you choose to do so, drop me a visitor message.

I will try to help.

Hi caffeinemachine,

Thanks for being willing to help ... appreciate it ...

I have been reflecting on what you have said about the length of the post ... but ... I do not think I can express it in a shorter post ...

Note a couple of points ...

1. The uploads are only needed for a reader to check the notation and to get an idea of the context ... they can be skimmed or even ignored by the reader with a good knowledge of implicit differentiation (which I suspect will include a number of MHB members) ... so a number of readers will only have to look at what I typed ...

2. I am only asking for someone to read what I have typed/written and confirm it is OK ...

Thanks again ...

Peter
 

FAQ: Analysis of Implicit Differentiation

What is implicit differentiation?

Implicit differentiation is a method used in calculus to find the derivative of a function that is not expressed explicitly in terms of one variable. It allows us to find the rate of change of a relationship between two variables, even if one variable cannot be solved for directly.

When is implicit differentiation used?

Implicit differentiation is used when a function is expressed in an implicit form, meaning that one variable cannot be easily isolated on one side of the equation. It is also used when finding the derivative of a function that contains both dependent and independent variables.

How does implicit differentiation differ from explicit differentiation?

Explicit differentiation involves finding the derivative of a function that is expressed explicitly in terms of one variable. This can be done using the basic rules of differentiation. On the other hand, implicit differentiation involves finding the derivative of a function that is not expressed explicitly, and therefore requires the use of the chain rule.

What are the steps for performing implicit differentiation?

The steps for performing implicit differentiation are as follows:

  1. Differentiate both sides of the equation with respect to the independent variable.
  2. Use the chain rule to differentiate any terms that contain dependent variables.
  3. Put all terms with the derivative of the dependent variable on one side of the equation.
  4. Solve for the derivative of the dependent variable.

What are some real-life applications of implicit differentiation?

Implicit differentiation is used in many real-life applications, including physics, engineering, and economics. For example, it can be used to find the rate of change of a relationship between two variables in a physics problem, or to optimize a function in an engineering design. In economics, implicit differentiation can be used to analyze the relationship between two variables, such as supply and demand, and determine the optimal point for maximum profit.

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