Linear algebra invertible transformation of coordinates

In summary: This is an easy task if the transformation is linear, but can be more difficult if the transformation is nonlinear.
  • #1
LCSphysicist
645
161
Homework Statement
Under what conditions is a coordinate transformation invertible in a neighborhood of some point?
Relevant Equations
N.
##A^{x'} = T(A^{x})##, where T is a linear transformation, in such way maybe i could express the transformation as a changing of basis from x to x' matrix:
##A^{x} = T_{mn}(A^{x'})##, in such conditions, i could say det ##T_{mn} \neq 0##. But how to deal with, for example, ##(x,y) -> (e^x,e^y)## ?
 
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  • #2
Think about the Jacobian.
 
  • #3
LCSphysicist said:
Homework Statement:: Under what conditions is a coordinate transformation invertible in a neighborhood of some point?
Relevant Equations:: N.

##A^{x'} = T(A^{x})##, where T is a linear transformation, in such way maybe i could express the transformation as a changing of basis from x to x' matrix:
##A^{x} = T_{mn}(A^{x'})##, in such conditions, i could say det ##T_{mn} \neq 0##. But how to deal with, for example, ##(x,y) -> (e^x,e^y)## ?
If the transformation is linear, you used the determinant criterion to check bijectivity. If it is not linear, then you obviously cannot use theorems about linear transformations. In that case you will have to define the inverse function and show that it is a bijection.

There is a difference between a bijection and an isomorphism. Isomorphisms belong to a certain category, here the category of vector spaces. This requires linearity. If the category is e.g. the topological spaces, then continuity in both directions is required, in the case of smooth manifolds it is differentiability. A bijection on the other hand is merely an isomorphism on the category of sets, where no further structure is considered, just sets.

By allowing an arbitrary coordinate transformation, you changed from the category of vector spaces to the category of sets. This means you forgot all linear structures. So you have to show that there is a second transformation ##T'## such that ##T\circ T'## and ##T'\circ T## are both identities of the corresponding set of vectors.
 

1. What is a linear algebra invertible transformation of coordinates?

A linear algebra invertible transformation of coordinates is a mathematical process used to change the coordinates of a point from one coordinate system to another. It involves using a linear transformation matrix to map the coordinates of a point in one system to the coordinates of the same point in a different system.

2. Why is it important to use invertible transformations in linear algebra?

Invertible transformations are important in linear algebra because they allow us to easily convert between different coordinate systems and perform calculations in different spaces. They also help us to better understand the geometric properties of objects and systems by transforming them into simpler forms.

3. How do you determine if a transformation is invertible?

A transformation is invertible if it has a unique inverse, meaning that there is a corresponding transformation that can map the coordinates back to their original values. In linear algebra, this is often determined by checking if the transformation matrix has a non-zero determinant.

4. What is the difference between an invertible and non-invertible transformation?

An invertible transformation can be reversed, meaning that the original coordinates can be retrieved from the transformed coordinates. A non-invertible transformation, on the other hand, cannot be reversed and the original coordinates cannot be retrieved from the transformed coordinates.

5. How are invertible transformations used in real-world applications?

Invertible transformations are used in a variety of real-world applications, such as computer graphics, image processing, and data analysis. They are also used in physics and engineering to model and analyze systems in different coordinate systems, allowing for more efficient and accurate calculations and predictions.

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