What Does Commutation Mean for Matrices A and B?

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In summary: Is my checking correct ##A \cdot A^{-1} \cdot B \cdot B^{-1} = I_A \cdot I_B = I##?Many thanks!Yes, but a bit short. The long version is:\begin{align*}(A\cdot B) \cdot (A\cdot B)^{-1}&=(A\cdot B) \cdot (B^{-1}\cdot A^{-1}) \\&= A \cdot (B \cdot (B^{-1}\cdot A^{-1}))\\&= A\
  • #1
ChiralSuperfields
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Homework Statement
Please see below
Relevant Equations
Please see below
For this,
1682209044875.png

Dose someone pleas know where they get ##C = CI## from?

Also,
1682209425978.png

What dose it mean when A and B commute?

Many thanks!
 
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  • #2
ChiralSuperfields said:
Homework Statement: Please see below
Relevant Equations: Please see below

For this,
View attachment 325350
Dose someone pleas know where they get ##C = CI## from?

Also,
View attachment 325351
What dose it mean when A and B commute?

Many thanks!
Commuting matrices means that ##A\cdot B = B\cdot A.## Most matrices do not commute. That means
$$
(A\cdot B)^{-1} =B^{-1} \cdot A^{-1} \neq A^{-1}\cdot B^{-1} = (B\cdot A)^{-1}
$$

Inversion and transposition, too, change the order. You can see this by checking ##(A\cdot B)\cdot (A\cdot B)^{-1} =I.##
 
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  • #3
fresh_42 said:
Commuting matrices means that ##A\cdot B = B\cdot A.## Most matrices do not commute. That means
$$
(A\cdot B)^{-1} =B^{-1} \cdot A^{-1} \neq A^{-1}\cdot B^{-1} = (B\cdot A)^{-1}
$$

Inversion and transposition, too, change the order. You can see this by checking ##(A\cdot B)\cdot (A\cdot B)^{-1} =I.##
Thank you for your reply @fresh_42 !

Is my checking correct ##A \cdot A^{-1} \cdot B \cdot B^{-1} = I_A \cdot I_B = I##?

Many thanks!
 
  • #4
ChiralSuperfields said:
Thank you for your reply @fresh_42 !

Is my checking correct ##A \cdot A^{-1} \cdot B \cdot B^{-1} = I_A \cdot I_B = I##?

Many thanks!
Yes, but a bit short. The long version is:

\begin{align*}
(A\cdot B) \cdot (A\cdot B)^{-1}&=(A\cdot B) \cdot (B^{-1}\cdot A^{-1}) \\
&= A \cdot (B \cdot (B^{-1}\cdot A^{-1}))\\
&= A\cdot (( B\cdot B^{-1})\cdot A^{-1})\\
&= A\cdot (I\cdot A^{-1})\\
&= A \cdot A^{-1} \\
&= I
\end{align*}
This proves by using the associative law of multiplication that ##B^{-1}\cdot A^{-1}## is a inverse of ##(AB)^{-1}.##

I leave it to you to show that there cannot be more than one inverse, making ##B^{-1}\cdot A^{-1}## the inverse of ##(AB)^{-1}.## Same with the identity matrix. There can only be one so we do not need to distinguish between ##I_A## and ##I_B## or between left-identity ##I_L\cdot A=A## and right-identity ##A\cdot I_R=A.## Both are the same. This can also be proven.

These proofs are a bit like a puzzle playing around with the associative, possibly distributive law. A nice Sunday afternoon exercise. The trick is to proceed step by step and only use these laws plus the definitions, e.g. that ##I_L\cdot A=A## and ##A\cdot I_R=A.## Show that ##I_L=I_R\,!##
 
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1. What is an inverse matrix?

An inverse matrix is a square matrix that, when multiplied by the original matrix, results in the identity matrix. In simpler terms, it is the "opposite" of the original matrix.

2. Why is an inverse matrix important?

An inverse matrix is important because it allows us to solve systems of linear equations, which are commonly used in many fields of science and mathematics. It also has applications in computer graphics, cryptography, and data analysis.

3. How do you find the inverse of a matrix?

To find the inverse of a matrix, you can use the Gauss-Jordan elimination method or the adjugate matrix method. Both methods involve performing a series of mathematical operations on the original matrix to transform it into its inverse.

4. What properties does an inverse matrix have?

An inverse matrix has several important properties, including: it is unique, it has the same dimensions as the original matrix, and its determinant is the reciprocal of the original matrix's determinant.

5. Can every matrix have an inverse?

No, not every matrix has an inverse. For a matrix to have an inverse, it must be a square matrix (same number of rows and columns) and its determinant must not be equal to zero. If these conditions are not met, the matrix does not have an inverse.

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