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$A$ is an $m \times n$ matrix with a singular value decomposition $A=U \Sigma V^{T},$ where $U$ is an $m \times m$ orthogonal matrix, $\Sigma$ is an $m \times n$ 'diagonal" matrix with $r$ positive entries and no negative entries, and $V$ is an $n \times n$ orthogonal matrix. Justify each answer.
Show that if $P$ is an orthogonal $m \times m$ matrix, then $P A$ has the same singular values as $A .$
Hence, the matrices $A$ and $P A$ have the same singular values.
Algebra
Chapter 7
Symmetric Matrices and Quadratic Forms
Section 4
The Singular Value Decomposition
Introduction to Matrices
Missouri State University
McMaster University
Idaho State University
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cakes for prom 20. We have Hey, she said. Or 12 in all. Or telling a matrix. Now we want to shoot that P Times say, has the same singular values as a now, by our assumption can be decomposed as you times seek my times. U transpose so pee off, eh can be reading There's he times you time Sigma times transpose Now Excuse me Now I know what he said since we know peace and on 1000 matrix and you is an orthogonal matrix. So we combine the product off p and you. Then we get a singular value decomposition of P and a p times safe. But at the same time between we observed these singular values that matrix Seema Sigma doesn't change. So that means our singular values will be the same. So we're done.
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