SVD Decomposition (Not in Base Package)

Computes the singular value decomposition (SVD) of the m × n matrix A. You can use this polymorphic VI to compute the SVD of a real matrix or a complex matrix. The data type you wire to the A input determines the polymorphic instance to use.

Real SVD Decomposition

	A is an m × n matrix with m rows and n columns.
	singular values only? specifies whether to compute only the singular values. The default is FALSE. When singular values only? is TRUE, the VI does not compute Matrix U and Matrix V.
	SVD Option specifies how the VI performs the decomposition. 0 Thin (default)—Decomposes an m × n matrix as the multiplication of matrix U (m × min(m,n)), S (min(m,n) × min(m,n)), and conjugated transpose of V (n × min(m,n)). 1 Full—Decomposes an m × n matrix as the multiplication of matrix U (m × m), S (m × n), and conjugated transpose of V (n × n).
	Vector S returns the singular values of A. The values in Vector S are the diagonal elements of Matrix S.
	Matrix U returns the U matrix of the SVD results.
	Matrix S returns the S matrix of the SVD results. Matrix S is a diagonal matrix whose diagonal elements are the values from Vector S.
	Matrix V returns the V matrix of the SVD results.
	error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster.

Complex SVD Decomposition

	A is an m × n matrix with m rows and n columns, where m is greater than n. If A has m < n, transpose A before you call this VI. Alternatively, you can create rows of zeros underneath the nonzero rows in A until A becomes square and then call this VI.
	singular values only? specifies whether to compute only the singular values. The default is FALSE. When singular values only? is TRUE, the VI does not compute Matrix U and Matrix V.
	SVD Option specifies how the VI performs the decomposition. 0 Thin (default)—Decomposes an m × n matrix as the multiplication of matrix U (m × min(m,n)), S (min(m,n) × min(m,n)), and conjugated transpose of V (n × min(m,n)). 1 Full—Decomposes an m × n matrix as the multiplication of matrix U (m × m), S (m × n), and conjugated transpose of V (n × n).
	Vector S returns the singular values of A. The values in Vector S are the diagonal elements of Matrix S.
	Matrix U returns the U matrix of the SVD results.
	Matrix S returns the S matrix of the SVD results. Matrix S is a diagonal matrix whose diagonal elements are the values from Vector S.
	Matrix V returns the V matrix of the SVD results.
	error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster.