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Math and science::Algebra
Singular value decomposition
Singular value decomposition (SVD)
Let A be an \( m \times n \) matrix. SVD decomposes \( A \) as:
[\[
A = \;\; ?
\]]
where \( U \) and \( V \) are [what type?] matrices and \(
\Sigma \) is a [what type?] matrix with non-negative real
numbers on the diagonal. The diagonal entries of \( \Sigma \) are called
the singular values of \( A \).
Assuming this decomposition of \( A \) is possible, how can we
find \( U \), \( \Sigma \), and \( V \)?
What is a geometric interpretation of the SVD?
