Can this type of transformation be non-linear?

In summary, the conversation discusses deriving a Lorentz transformation without using the now out of favor i^2=-1 and the possibility of the transformation being non-linear. It is concluded that for all straight lines in one coordinate system to be transformed into straight lines in the other, the transformation must be linear, and it can be shown using mathematical terms.
  • #1
snoopies622
840
28
I've finally worked out a derivation of the Lorentz transformation that doesn't use the now out of favor [itex]i^2=-1[/itex], but it still has one weak spot: it assumes that the transformation is linear. It seems quite reasonable to me that it would be linear since it has to graph straight lines on to straight lines (since the laws of mechanics should be the same in both reference frames) but how can I go from that fact to

x' = Ax + Bt
t' = Dx + Et

where A,B,D and E are constants without any doubt? Is it mathematically possible for a transformation that requires any straight line in one coordinate system to become a straight line in the other coordinate system to assume some other, non-linear form?
 
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  • #2
The transformation you wrote is linear by construction: ##\begin{bmatrix}x'\\t' \end{bmatrix}=\begin{bmatrix}A&B\\ D&E \end{bmatrix}\cdot \begin{bmatrix}x\\t \end{bmatrix}##. To get something non-linear you will have to use non-linear terms.

Are you talking about one straight line or all straight lines? You could add a constant term, so it still transforms lines into lines, but isn't linear anymore, however, affine linear.
 
  • #3
I start with the premise that all straight lines in (x,t) are transformed into straight lines into (x',t') and vice versa and that (0,0) in one coordinate system is (0,0) in the other. Does it then follow that the transformation must look like

x' = Ax + Bt
t' = Dx + Et

where A,B, D, E are constants, and how do I know this for certain? Thanks.
 
  • #4
snoopies622 said:
I start with the premise that all straight lines in (x,t) are transformed into straight lines into (x',t') and vice versa and that (0,0) in one coordinate system is (0,0) in the other. Does it then follow that the transformation must look like

x' = Ax + Bt
t' = Dx + Et

where A,B, D, E are constants, and how do I know this for certain? Thanks.
You want to show, that for a given transformation ##f## it has to hold: ##f##(straight)=straight which means ##f(\lambda \vec{a}+\mu \vec{b})= \lambda f(\vec{a})+\mu f(\vec{b})## for all ##\vec{a},\vec{b},\lambda,\mu\,.##
 

1. Can non-linear transformations be applied to any type of data?

Yes, non-linear transformations can be applied to both numerical and categorical data. However, the specific type of transformation that is appropriate will depend on the nature of the data and the research question being investigated.

2. What is the difference between linear and non-linear transformations?

The main difference between linear and non-linear transformations is that linear transformations result in a straight-line relationship between the input and output variables, while non-linear transformations produce a curved relationship. Linear transformations are often simpler and easier to interpret, but non-linear transformations can capture more complex relationships in the data.

3. How do I know if a non-linear transformation is necessary for my data?

There are a few different ways to determine if a non-linear transformation is necessary for your data. One approach is to plot the relationship between the input and output variables and look for any patterns or trends. If the relationship appears to be curved rather than linear, a non-linear transformation may be necessary. Another approach is to use statistical tests or model diagnostics to assess the linearity of the relationship.

4. Can non-linear transformations improve the performance of my model?

Yes, in some cases, using a non-linear transformation can improve the performance of a model. This is because non-linear transformations can help to capture more complex relationships in the data that may not be captured by a linear model. However, it is important to carefully select the appropriate transformation and to consider the interpretability of the model.

5. Are there any limitations to using non-linear transformations?

While non-linear transformations can be useful in certain situations, they also have some limitations. For example, they may be more difficult to interpret compared to linear transformations, and they may not always improve the performance of a model. Additionally, using a non-linear transformation may require more data or more complex modeling techniques, which could increase the computational cost of the analysis.

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