Undergrad What is the Role of Direct Calculation in Planar Rotation Commutativity?

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Direct calculation is essential in demonstrating the commutativity of planar rotations, which involves showing that the product of two rotation matrices, ##ab##, equals the product in the reverse order, ##ba##. This process typically requires working through the multiplication of rotation matrices that represent complex numbers. The discussion emphasizes that the commutativity holds true because the imaginary unit ##\mathbf i## commutes with itself and the identity matrix ##\mathbf I##. It is noted that this property is constrained to two dimensions, specifically on the unit circle, as higher dimensions complicate the definition of rotational axes. Understanding these principles is crucial for grasping the foundational aspects of planar rotation commutativity.
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Commutativity for planar rotations follows from a direct calculation.
What does 'direct calculation' mean?
 
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Take two rotations ##a,b## and show that ##ab=ba##.

In the usual setting, this will be showing that two linear maps commute or that two rotation matrices commute.
 
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I think it means actually calculating ##ab## and ##ba##, working out the products of the rotation matrices for general rotations.
 
A nice way to do this, is to recognize that you are in effect verifying that complex numbers commute (and that this holds even when they are represented as 2x2 matrices). So consider a complex number

##a_1 + b_1 i##, given as

##\begin{bmatrix}
a_1 & -b_1\\
b_1 & a_1
\end{bmatrix}= a_1
\begin{bmatrix}
1 & 0\\
0 & 1
\end{bmatrix} +
b_1 \begin{bmatrix}
0 & -1\\
1 & 0
\end{bmatrix} = a_1 \mathbf I + b_1 \mathbf i##

now multiply by some other complex number ##a_2 + b_2 i## and see that

##\big(a_1 \mathbf I + b_1 \mathbf i\big)\big(a_2 \mathbf I + b_2 \mathbf i\big) = \big(a_2 \mathbf I + b_2 \mathbf i\big) \big(a_1 \mathbf I + b_1 \mathbf i\big) ##

because ##\mathbf i## commutes with scaled forms of itself and the identity matrix ##\mathbf I## commutes with everything. Since you are talking about rotation matrices, you are constraining yourself to a determinant of 1 here (aka complex numbers on the unit circle).
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edit: cleaned up some table formatting issues based on below hint
 
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@StoneTemplePython
This was created by PF5, it "thought" you were making a table, so it wrapped TABLE HTML tags around the area.
New feature. If you get them and do not want them, toogle into bbcode (gear-like icon on the toolbar, far right).
Remove the two tags - most HTML tags have start and end like this [STARTME] ...blah blah [/STARTME].

You can do this on your next post, I think the one post above is old enough to have locked you out of edit. If you want I can clean them up, PM me.
 
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Notice commutativity applies only in one dimension, meaning on the circle. Once you go into higher dimensions things become more complicated , where you have to define your axis of rotation.
 
Here is a little puzzle from the book 100 Geometric Games by Pierre Berloquin. The side of a small square is one meter long and the side of a larger square one and a half meters long. One vertex of the large square is at the center of the small square. The side of the large square cuts two sides of the small square into one- third parts and two-thirds parts. What is the area where the squares overlap?

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