Inverse function theorem over matrices

In summary, the conversation discusses a function involving squaring matrices and the possibility of using a new dot product to redefine the function. The concept of the Jacobian is also mentioned, and the question of whether the Inverse Function Theorem applies to the function at hand is posed. The conversation also explores the idea of finding a unique matrix that squares to a given matrix near the identity. Modifications to the Jacobian are suggested to find the correct solution.
  • #1
brunob
15
0

Homework Statement


I have a function [itex]f:M_{n×n} \to M_{n×n} / f(X) = X^2[/itex].

The questions
Is valid the inverse function theorem for the identity matrix? It talks about the Jacobian at the identity, but I have no idea how get a Jacobian of that function. Can I see the matrices as vectors and redefine the function as [itex]f:R^{n^2} \to R^{n^2} / f(x) = x^2[/itex] using a new dot product that represents the matrix multiplication?

Also, how can I prove that if a matrix [tex]Y[/itex] is near to the identity then [itex]\exists ! X / X^2 = Y[/itex] ?

Thanks!
 
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  • #2
brunob said:

Homework Statement


I have a function [itex]f:M_{n×n} \to M_{n×n} / f(X) = X^2[/itex].

I don't know why you use / here. It's better to just write "such that". (sorry, nitpicky)

The questions
Is valid the inverse function theorem for the identity matrix? It talks about the Jacobian at the identity, but I have no idea how get a Jacobian of that function. Can I see the matrices as vectors and redefine the function as [itex]f:R^{n^2} \to R^{n^2} / f(x) = x^2[/itex] using a new dot product that represents the matrix multiplication?

The dot product has nothing to do with things here. You need to find the Jacobian somehow of squaring matrices. Now, if we look to the one-dimensional case, we have the map

[tex]f:\mathbb{R}\rightarrow \mathbb{R}:x\rightarrow x^2[/tex]

The Jacobian of that is just the derivative
[tex]J(x):\mathbb{R}\rightarrow \mathbb{R}:h\rightarrow 2xh[/tex]

This suggests that in the general case we have a map

[tex]f:\mathbb{R}^{n^2}\rightarrow \mathbb{R}^{n^2}:X\rightarrow X^2[/tex]

and that the Jacobian would be
[tex]J(X):\mathbb{R}^{n^2}\rightarrow \mathbb{R}^{n^2}:H\rightarrow 2XH[/tex]
Of course, you must check this first. Given a multivariable map ##f## and a matrix ##A(x)## at every point, you have that this is the Jacobian if

[tex]\lim_{h\rightarrow 0} \frac{f(x+h) - f(x) - A(x)h}{\|h\|} = 0[/tex]

So you must check that this holds for ##A(X) = 2X##. Thus

[tex]\lim_{H\rightarrow 0} \frac{(X+H)^2 -X^2 - 2XH}{\|H\|} = 0[/tex]

If you try to caluclate this, you will find that this is not true. So ##A(X) = 2X## is not the right Jacobian. Can you make a small modification that will provide the right Jacobian?

Also, how can I prove that if a matrix [tex]Y[/itex] is near to the identity then [itex]\exists ! X / X^2 = Y[/itex] ?

What do you get after applying the Inverse Function Theorem on ##f(X) = X^2##?
 

What is the inverse function theorem over matrices?

The inverse function theorem over matrices is a mathematical theorem that states under certain conditions, a function that maps from one matrix space to another can be inverted, meaning that there exists a function that maps back from the second space to the first space.

What are the conditions for the inverse function theorem to hold?

For the inverse function theorem to hold, the function must be differentiable and have a non-zero Jacobian determinant at every point in the domain. Additionally, the function must be a local diffeomorphism, meaning that it is both one-to-one and onto locally.

What is the importance of the inverse function theorem?

The inverse function theorem is important because it allows us to solve equations involving matrices by finding the inverse function. This can be useful in many areas of mathematics and science, including in optimization problems and in solving systems of equations.

How is the inverse function theorem related to the inverse function of real numbers?

The inverse function theorem for matrices is a generalization of the inverse function theorem for real numbers. Just like how the inverse function of a real number undoes the original function, the inverse function for matrices undoes the original matrix function.

Can the inverse function theorem be applied to non-square matrices?

No, the inverse function theorem only applies to square matrices. This is because the inverse of a non-square matrix is not unique and therefore cannot be used to map back to the original matrix space.

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