Linear transformation of a given coordinate

In summary, the conversation discusses the concept of weights in a vector space and how they do not convert linearly when basis sets convert linearly. This is because the transformation from one basis to another involves changing from a linear coordinate system (Cartesian coordinates) to a non-linear one (cylindrical coordinates). This results in non-linear transformations and explains why the weights do not convert linearly.
  • #1
kidsasd987
143
4
I have a question about weights of a basis set with respect to the other basis set of one specific vector space.
It seems the weights do not covert linearly when basis sets convert linearly. I've got this question from the video on youtube "linear transformation"



Let's consider a vector space V spanned upon field K.

Then its element x∈V can be expressed in the form of a linear combination with a given basis set and its corresponding weights.

x=ΣaixiNow if we were to express a vector x with respect to the new basis x_hati,
we would express the same vector as

x=Σbix_hati
We can derive the relation

Λ(xi)=x_hati
bi=Λ-1ai
I did this to cylindrical coordinate, and because
https://wikimedia.org/api/rest_v1/media/math/render/svg/cf553bbb290f2b6ad76c9cce12f8807d43ab09ee

and according to the equation from video
bi=Λ-1ai

inverted matrix (transpose of matrix above applied to <x,y,z> should give converted weight bi but this isn't the case.
rho=sqrt(x^2+y^2) and phi=tan^-1(y/x) and they are not linear functions.could anyone tell me why is it so?
 
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  • #2
When people talk about a linear transformation between two basis which leads to different coordinates (weights), then it is meant to transform one basis vector linearly to another. Let's keep it simple and consider a two dimensional real vector space: a sheet of graph paper.

You can scale it by one box per centimeter on your graph paper or be two boxes or any other multiple or even different multiples for the ##x## and ##y-##axis. You could even chose the ##y-##axis to be the diagonal instead of the perpendicular axis. All these are linear transformations from one basis to another: ##\begin{bmatrix}2&0\\0&2\end{bmatrix}\, , \,\begin{bmatrix}4&0\\0&1\end{bmatrix}\, , \,\begin{bmatrix}1&0\\1&1\end{bmatrix}## or whatever.

This changes if (at least) one coordinate is transformed non-linearly: bent into a circle as in your example or another example would be a logarithmic scale: ##\log x' \leftrightarrow x \nLeftrightarrow x'=Ax## because ##\log (x'_1+x'_2) \neq c_1\log x_1 +c_2 \log x_2##. In addition: Which angle has the origin in cylindrical coordinates? So you changed from something linear (Cartesian coordinates) to something non-linear (cylindrical coordinates) which thus cannot be done by something (function) linear. One could say you changed the nature of the coordinate system.
 
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Related to Linear transformation of a given coordinate

What is a linear transformation of a given coordinate?

A linear transformation is a mathematical operation that maps one set of coordinates to another set of coordinates in a straight line. It is often used to transform data or solve equations in physics, engineering, and other scientific fields.

What are some common examples of linear transformations?

Some common examples of linear transformations include scaling, rotation, shearing, and translation. These transformations are used to manipulate coordinates in 2D or 3D space.

How is a linear transformation represented mathematically?

A linear transformation can be represented using a matrix, where the original coordinates are multiplied by the matrix to get the transformed coordinates. The matrix can also be used to perform multiple transformations at once.

What is the importance of linear transformations in scientific research?

Linear transformations are essential in scientific research as they allow researchers to analyze and interpret data in a more meaningful way. They also help in solving equations and modeling real-life systems.

What are the limitations of linear transformations?

One limitation of linear transformations is that they can only transform coordinates in a straight line. They cannot account for non-linear relationships or complex patterns in data. Additionally, linear transformations may not accurately represent all real-life systems, as some may exhibit non-linear behavior.

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