![]() In linear algebra, the singular value decomposition ( SVD) is a factorization of a real or complex matrix into a rotation, followed by a rescaling followed by another rotation. Practically, however, matrices are usually referred to in the documentation as having at least 2 columns and 2 rows, so I would rather call xL and xU vectors.
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