Performs the singular value decomposition (SVD) of a given m-by-n real matrix A, with m>n. Details
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A is an m-by-n matrix with m is greater than n, where m represents the number of rows in A, and n represents the number of columns in A. If A has m<n, transpose A before you call this VI. Or, you can create rows of zeroes underneath the nonzero rows in A, until A becomes square, and then call this VI. |
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U is an m-by-n matrix, which contains n orthogonal columns. |
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S is an array, which contains the number of n singular values of A in decreasing order. |
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V is an n-by-n orthogonal matrix. |
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error returns any error or warning condition from the VI. |
SVD produces three matrices U,, and V so that
where U and are orthogonal matrices,
is an n-by-n diagonal matrix with the elements of array S on the diagonal in decreasing order.