MHB Inverse of Giant $7 \times 7$ Matrix: Tips & Tricks

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To find the inverse of the given $7 \times 7$ matrix, it is noted that the matrix is orthogonal, meaning its inverse is simply its transpose. While some participants suggest using the Gauss-Jordan method, others argue it may not be as tedious as expected, with potential row swaps simplifying the process. A participant successfully identified a less tedious solution by expanding the diagonal entries of the identity matrix to derive the non-zero elements of the inverse. Overall, leveraging the orthogonal property of the matrix provides a straightforward approach to finding its inverse.
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How do I go about find out the inverse of this giant $7 \times 7$ matrix? Do I need to evaluate it using the tedious Gauss-Jordan method? Any property, short-cut, trick that I can take advantage of?

$$\begin{pmatrix}
0 &1 &0 &0 &0 &0 &0\\
0 &0 &0 &1 &0 &0 &0\\
0 &0 &0 &0 &0 &0 &1\\
0 &0 &0 &0 &0 &1 &0\\
0 &0 &1 &0 &0 &0 &0\\
0 &0 &0 &0 &1 &0 &0\\
1 &0 &0 &0 &0 &0 &0\\
\end{pmatrix}$$

Your time and gracious help is very much appreciated? Thank you. ~MA
 
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Lets call the matrix you gave $A$. What is the transpose of $A$? That's your answer but I'm not immediately sure how to prove that.
 
Are you supposed to be finding a shortcut or fast method? It would seem unlikely that finding the inverse of a matrix of this size would be a homework task to be done by hand?
 
Hey MaryAnn! ;)

Are you familiar, or do your notes say something about an Orthogonal matrix?
 
I like Serena said:
Hey MaryAnn! ;)

Are you familiar, or do your notes say something about an Orthogonal matrix?

No, we don't have it. I checked textbook's index but could not find any orthogonal matrix entry. But anyway, I found out the least tedious solution: Assuming the matrix is $X$ and its inverse is $Y$, then $XY = I$. Then using the definition of multiplication of matrix, I expanded the seven entries of the diagonal of $I$ (since they are all non-zero) to come up with the $y_{ij}$ that are non-zero. The rest of $y_{ij}$ are zero. Thank you though for pitching in, thanks for your gracious help and time. ~MA

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greg1313 said:
Lets call the matrix you gave $A$. What is the transpose of $A$? That's your answer but I'm not immediately sure how to prove that.

I did that, but its transpose did not take me anywhere. Thank you though for your gracious help, and time. ~MA

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Joppy said:
Are you supposed to be finding a shortcut or fast method? It would seem unlikely that finding the inverse of a matrix of this size would be a homework task to be done by hand?

I think I found the least tedious solution, see my response to I Like Serena. Thank you though for your gracious help, and for your time. ~MA
 
MaryAnn said:
No, we don't have it. I checked textbook's index but could not find any orthogonal matrix entry. But anyway, I found out the least tedious solution: Assuming the matrix is $X$ and its inverse is $Y$, then $XY = I$. Then using the definition of multiplication of matrix, I expanded the seven entries of the diagonal of $I$ (since they are all non-zero) to come up with the $y_{ij}$ that are non-zero. The rest of $y_{ij}$ are zero. Thank you though for pitching in, thanks for your gracious help and time. ~MA

That works as well.
For the record, your matrix is orthogonal because each column vector has unit length, and because each pair of column vectors is orthogonal.
As a consequence the inverse is simply the transpose as greg1313 mentioned. (Mmm)
 
It doesn't look to me like "Gauss-Jordan" will be all that tedious. You should be able to do some obvious "swaps" of two rows to get the matrix to the identity matrix,
 
I like Serena said:
That works as well.
For the record, your matrix is orthogonal because each column vector has unit length, and because each pair of column vectors is orthogonal.
As a consequence the inverse is simply the transpose as greg1313 mentioned. (Mmm)

Thank you again for your gracious help. ~MA

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HallsofIvy said:
It doesn't look to me like "Gauss-Jordan" will be all that tedious. You should be able to do some obvious "swaps" of two rows to get the matrix to the identity matrix,

Thank you for your gracious comment. ~MA
 

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