-*> \par Purpose:
-* =============
-*>
-*> \verbatim
-*>
-*> ZGESVDX computes the singular value decomposition (SVD) of a complex
-*> M-by-N matrix A, optionally computing the left and/or right singular
-*> vectors. The SVD is written
-*>
-*> A = U * SIGMA * transpose(V)
-*>
-*> where SIGMA is an M-by-N matrix which is zero except for its
-*> min(m,n) diagonal elements, U is an M-by-M unitary matrix, and
-*> V is an N-by-N unitary matrix. The diagonal elements of SIGMA
-*> are the singular values of A; they are real and non-negative, and
-*> are returned in descending order. The first min(m,n) columns of
-*> U and V are the left and right singular vectors of A.
-*>
-*> ZGESVDX uses an eigenvalue problem for obtaining the SVD, which
-*> allows for the computation of a subset of singular values and
-*> vectors. See DBDSVDX for details.
-*>
-*> Note that the routine returns V**T, not V.
-*> \endverbatim
+* Purpose
+* =======
+*
+* ZGESVDX computes the singular value decomposition (SVD) of a complex
+* M-by-N matrix A, optionally computing the left and/or right singular
+* vectors. The SVD is written
+*
+* A = U * SIGMA * transpose(V)
+*
+* where SIGMA is an M-by-N matrix which is zero except for its
+* min(m,n) diagonal elements, U is an M-by-M unitary matrix, and
+* V is an N-by-N unitary matrix. The diagonal elements of SIGMA
+* are the singular values of A; they are real and non-negative, and
+* are returned in descending order. The first min(m,n) columns of
+* U and V are the left and right singular vectors of A.
+*
+* ZGESVDX uses an eigenvalue problem for obtaining the SVD, which
+* allows for the computation of a subset of singular values and
+* vectors. See DBDSVDX for details.
+*
+* Note that the routine returns V**T, not V.