Proving the Invertibility of Derivative Matrices for Inverse Functions

In summary, the conversation discusses the proof that the matrix Df(x_0) is invertible with inverse Dg(f(x_0)) for each x_0 in the open subset U of R^n and V of R^m, under the assumption that f: U -> V is a C^1 function with a C^1 inverse g: V -> U. The conversation also touches on the conclusion that n = m, and provides a proof for this result.
  • #1
Benny
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0
Hi, I'm having trouble getting started on the following question and would like some help.

Q. Let U be an open subset of R^n and V be an open subset of R^m. Assume that [itex]f:U \to V[/itex] is a C^1 function with a C^1 inverse [itex]g:V \to U[/itex]. (Thus (f o g) is the identity function [itex]1_V :V \to V[/itex] and (g o f) is the identity function [itex]1_U :U \to U[/itex].)

a) Show that the matrix [itex]Df\left( {\mathop {x_0 }\limits^ \to } \right)[/itex] is invertible with inverse [itex]Dg\left( {f\left( {\mathop {x_0 }\limits^ \to } \right)} \right)[/itex] for each x_0 in U.

I'm thinking that I might need to use matrix multiplication for example AA^-1 = I or something like that. I know that the derivative matrices for f and g evaluated at x_0 are defined since they are both C^1. Those are just some rough ideas and I don't know where to begin. Is there something obvious that I should I work with to get started? Any help would be good thanks.
 
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  • #2
Just apply the chain rule to (f o g)(x)= x.
 
  • #3
I considered it this way.

By the chain rule (g o f)'(x) = Dg(f(x))Df(x)

(g o f) is the identity function which is (I think) linear. So I write (g o f)(x) = Ix. The derivative of a linear function is just the matrix.

So I = Dg(f(x))Df(x). If that takes care of the inverse part then is there anything else I need to do in order to show that Df(x) is invertible? If CD = I then does it automatically follow that C is invertible?
 
  • #4
Well, you should also show that DC = I... but since Df is not nxn I don't really know what you're doing.
 
  • #5
Ok I see your point.

If I use (f o g)(x) = x and I differentiate both sides I obtain Df(g(x))Dg(x) = 0 for a fixed vector x. Differentiating (g o f)(x) = x I obtain Dg(f(x))D(g(x)) = 0 for a fixed vector x. So Df(g(x))Dg(x) = Dg(f(x))Df(x). That doesn't appear to be going anywhere. :confused:
 
  • #6
No, it is not true in general that Df(g(x))Dg(x) = Dg(f(x))Df(x) because they are matrices of different dimensions.
 
  • #7
The next part of the question says to conclude that n = m. But since I can't use that in this question, how would I even show that Df(x_0) is invertible with inverse Dg(f(x_0))?

To show the given result for this question I would need to arrive at Dg(f_0))Df(x_0) = I = Df(x_0)Dg(f(x_0)). But this doesn't even make sense if the matrices aren't square.

For example its like saying AB = I = BA implies A^-1 = B even though A and B aren't square matrices. I just don't see how this question is supposed to be done.

The definition that I have seen for invertible matrices require the matrices themselves to be square. So the statement "AB = I = BA implies A^-1 = B" only makes sense when A and B are square. In the case of my question Df(x_0) is not square (or at least I can't assume it is). So I don't know what to do.

To clarify my question. It consist of two parts.

a) The question in my original post,
b) Conclude that n = m. (a result which I can't really assume in part 'a').
 
  • #8
Oh, my bad...

From what you were saying before you have
f o g(x) = x
g o f(x) = x
Df(g(x))g(x) = I
Dg(f(x))f(x) = I
with I the same in both because they are both Dx and x has the same dimensions in both
So you really can conclude, as you said
Df(g(x))Dg(x) = Dg(f(x))Df(x)
Therefore DfDg and DgDf have the same dimensions, and n = m, and the inverse of Df(x) is Dg(f(x)).
 
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  • #9
Hmm but f o g: V -> V where x is not in V so (f o g)(x) doesn't make sense. I can't assume that n = m for part (a) (the question in my original post). But then how can I possibly show that Df(x_0) is invertible?
 
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  • #10
O.K. Well, I have screwed this up twice in one thread, let's see if this is another strike.

Df(g(x))Dg(x) = Im
Dg(f(x))Df(x) = In

Now if n > m then dim(ker(Df(x))) is greater than 0 for every x, so (Df(x))v = 0 for some nonzero v, contradicting the second equation.
If m > n then dim(ker(Dg(x))) is greater than 0 for every x, so (Dg(x))v = 0 for some nonzero v, contradicting the first equation.
So m = n.
 
  • #11
Thanks for your response, it made things more clear. I got down to the two equations involving I_m and I_n as you did. But I just said that if n is not equal to m then one of the equations doesn't make sense. Your argument is much stronger. I'm weak on the linear algebra (can't remember quite a lot of stuff from last year) so I'll need to go over your working. Thanks again.
 

What are derivatives of inverses?

Derivatives of inverses refer to the rate of change or slope of the inverse function of a given function. They can be calculated using the inverse function theorem.

How do you find the derivative of an inverse function?

The derivative of an inverse function can be found by using the formula (f^-1)'(x) = 1 / f'(f^-1(x)), where f'(x) is the derivative of the original function and f^-1(x) is the inverse function.

What is the relationship between the derivatives of a function and its inverse?

The derivatives of a function and its inverse are reciprocals of each other. In other words, if the derivative of a function is 2, then the derivative of its inverse will be 1/2.

Can the derivative of an inverse function be negative?

Yes, the derivative of an inverse function can be negative. This indicates that the inverse function is decreasing at that point, while the original function is increasing.

Why are derivatives of inverses important in mathematics?

Derivatives of inverses are important in mathematics because they allow us to find the rate of change of inverse functions and can be used to solve various problems in calculus, such as optimization and related rates.

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