MHB Proving Matrix Equality Using Singular Value Decomposition

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To prove that \( AA^T = BB^T \) if and only if \( A = BO \) for some orthogonal matrix \( O \), singular value decomposition (SVD) is essential for the forward direction. Specifically, if \( AA^T = BB^T \), the singular values of \( A \) and \( B \) are equal, allowing the representation \( A = U\Sigma V^T \) and \( B = U\Sigma Q^T \). This leads to the conclusion that \( A = BO \) where \( O = QV^T \), confirming \( O \) is orthogonal. The reverse direction does not require SVD, as it can be shown directly that \( A = BO \) implies \( AA^T = BB^T \). Thus, SVD is primarily utilized for the forward proof in this context.
linearishard
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Hi, I have another question, if A and B are mxn matrices, how do I prove that $AA^T = BB^T$ iff $A = BO$ where $O$ is some orthogonal matrix? I think I need to use a singular value decomposition but I am not sure. Thanks!
 
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Can you at least prove the reverse direction, that is, if $A = BO$ for some orthogonal matrix $O$, then $AA^T = BB^T$? You don't need to use SVD for this.
 
Yeah I did that but it seemed too simple, my study guide says I should be using SVD. Is it actually unnecessary?
 
You use SVD for the forward direction, not the reverse direction.
 
what do you mean by that? What is the forward and reverse directions?
 
The forward direction: If $AA^T = BB^T$, then $A = BO$ where $O$ is some orthogonal matrix. The reverse direction: If $A = BO$ where $O$ is an orthogonal matrix, then $AA^T = BB^T$.
 
If $AA^T = BB^T$, then the singular values of $A$ are the singular values of $B$. You can write $A = U\Sigma V^T$ and $B = U\Sigma Q^T$ for some orthogonal matrices $U, V$, and $Q$. Then $A = BO$ where $O = QV^T$. Since the transpose and product of orthogonal matrices are orthogonal, $O$ is orthogonal.
 

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