Symmetry Operations of a Cube: Geometric Descriptions and Matrix Representations

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    Cube Symmetries
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Discussion Overview

The discussion revolves around the symmetry operations of a cube, focusing on geometric descriptions and matrix representations. Participants explore the properties of specific matrices and their implications for the symmetries of a cube, including rotations and reflections.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • Some participants propose defining the set of vertices of a cube using coordinates in the set $\{-1, 1\}$ and connecting points that differ by one coordinate.
  • There is a suggestion to verify if matrices representing symmetries are isometries that map cube vertices to themselves.
  • One participant illustrates that the matrix $d$ is orthogonal and thus an isometry, transforming vertices while preserving their coordinates in $\{-1, 1\}$.
  • Another participant discusses the matrix $s$, also showing it as an isometry and mapping vertices correctly.
  • Participants express uncertainty about how to find matrices that describe specific symmetries and whether to multiply all vertices by these matrices.
  • There are discussions on classifying matrices as rotations or reflections based on their geometric interpretations.
  • Some participants suggest finding eigenvalues and eigenvectors to classify the matrices, with one participant providing specific eigenvalues for matrix $d$.
  • Questions arise about the geometric interpretation of transformations and whether certain vectors remain unchanged under specific transformations.

Areas of Agreement / Disagreement

Participants generally agree on the definitions of symmetries and the properties of the matrices discussed, but there is no consensus on the specific methods for finding matrices that represent each symmetry or on the geometric interpretations of the transformations.

Contextual Notes

Participants express uncertainty regarding the relationship between eigenvalues and eigenvectors and their relevance to classifying the matrices. The discussion includes unresolved questions about the geometric implications of certain transformations.

  • #31
Klaas van Aarsen said:
The vector $v$ is fine.
The problem is that we have 2 possible choices for $v'$ and we need to pick the right one.
That is, the one such that $v\times v'$ is in the same direction as $u$. 🤔

Taking $v'=\left (0, 1, 1\right )$ we have the following:

We have the vector $w=u\times v=(1,1,-1)\times (-1,0,-1)=(-1,2,1)$ and we get $\displaystyle{w'=\|w\|\frac{u\times v'}{\|u\times v'\|}=\sqrt{6}\frac{(1,1,-1)\times (0, 1, 1)}{\|(1,1,-1)\times (0, 1, 1)\|}=\sqrt{6}\frac{(2,-1,1)}{\sqrt{6}}=(2,-1,1)}$.

The condition is now satisfied, isn't it? We have $w\cdot v'=3$.

So we get the matrix \begin{equation*}\begin{pmatrix}0 & 1 & 0 \\ 0 & 0 & -1 \\ -1 & 0 & 0\end{pmatrix}\end{equation*}

:unsure:
 
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  • #32
mathmari said:
The condition is now satisfied, isn't it? We have $w\cdot v'=3$.

So we get the matrix \begin{equation*}\begin{pmatrix}0 & 1 & 0 \\ 0 & 0 & -1 \\ -1 & 0 & 0\end{pmatrix}\end{equation*}
Yep. All correct. (Nod)
 

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