How to count all the orthogonal transformations?

In summary, there are four orthogonal transformations that can map the given plane and vector to their respective counterparts while keeping the determinant of the original matrix unchanged. These transformations are represented by the different values of ##A_{0}=diag(1,1,1)##, ##A_{0}=diag(-1,-1,1)##, ##A_{0}=diag(-1,1,-1)##, and ##A_{0}=diag(1,-1,-1)##.
  • #1
skrat
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Homework Statement


Find an orthogonal transformation ##\mathbb{R}^{3}\rightarrow \mathbb{R}^{3}## that map plane ##x+y+z=0## into ##x-y-2z=0## and vector ##v_{1}=(1,-1,0)## into ##(1,1,0)##. Count all of them!

Homework Equations


##A_{S}=PA_{0}B^{-1}##

The Attempt at a Solution


So basis ##B=\begin{bmatrix}
1 & 1 & 1\\
1& -1 & 1\\
1& 0& 2
\end{bmatrix}## and ##P=\begin{bmatrix}
1 & 1 & 1\\
-1& 1 & -1\\
-2& 0& 1
\end{bmatrix}## where the last vector in both basis is a vector product of ##n_{1} \times v_{1}##

##A_{0}=diag(1,1,1)## and ##A_{S}=PA_{0}B^{-1}##

Now, how do I count them? What do they represent - meaning, how do they differ from this one? o_O please help.
 
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  • #3
So whatever I do, ##det(A_{0})=1## has to stay the same, where ##A_{0}=diag(1,1,1)##. Meanin I could also use ##A_{0}=diag(-1,-1,1)## or ##A_{0}=diag(-1,1,-1)## or ##A_{0}=diag(1,-1,-1)##, right?

To sum up, there are 4 orthogonal transformations that do that?
 

Related to How to count all the orthogonal transformations?

1. How many orthogonal transformations are there?

There are infinitely many orthogonal transformations in the mathematical space. This is because any rotation, reflection, or combination of these operations can be considered an orthogonal transformation.

2. How do you count all the orthogonal transformations mathematically?

To count all the orthogonal transformations, we can use the concept of group theory. Orthogonal transformations form a group, which means they have certain properties and can be counted using mathematical techniques such as group multiplication tables and group orders.

3. What is the difference between an orthogonal transformation and a non-orthogonal transformation?

An orthogonal transformation preserves the length and angles of vectors, while a non-orthogonal transformation does not necessarily preserve these properties. In other words, an orthogonal transformation is a rotation or reflection, while a non-orthogonal transformation can also involve stretching, shearing, or other operations.

4. Can all orthogonal transformations be represented by matrices?

Yes, all orthogonal transformations can be represented by orthogonal matrices. These are square matrices with a determinant of 1 and satisfy the property that the inverse is equal to the transpose. The dimension of the matrix depends on the dimension of the space in which the transformation is taking place.

5. How are orthogonal transformations used in real-life applications?

Orthogonal transformations have various applications in fields such as computer graphics, physics, and engineering. They are used to rotate objects in 3D space, correct distortions in images, and analyze the behavior of physical systems. In addition, they play a crucial role in linear algebra and are fundamental in understanding the properties of vectors and matrices.

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