Comments fix to be able to generate the new layout and the corresponding Doxygen documentation

1 files changed, 54 insertions, 51 deletions

diff --git a/SRC/dgesvj.f b/SRC/dgesvj.f
index 5b40d0f2..03ac060e 100644
--- a/SRC/dgesvj.f
+++ b/SRC/dgesvj.f
@@ -39,57 +39,6 @@
 *  of SIGMA are the singular values of A. The columns of U and V are the
 *  left and the right singular vectors of A, respectively.
 *
-*  Further Details
-*  ~~~~~~~~~~~~~~~
-*  The orthogonal N-by-N matrix V is obtained as a product of Jacobi plane
-*  rotations. The rotations are implemented as fast scaled rotations of
-*  Anda and Park [1]. In the case of underflow of the Jacobi angle, a
-*  modified Jacobi transformation of Drmac [4] is used. Pivot strategy uses
-*  column interchanges of de Rijk [2]. The relative accuracy of the computed
-*  singular values and the accuracy of the computed singular vectors (in
-*  angle metric) is as guaranteed by the theory of Demmel and Veselic [3].
-*  The condition number that determines the accuracy in the full rank case
-*  is essentially min_{D=diag} kappa(A*D), where kappa(.) is the
-*  spectral condition number. The best performance of this Jacobi SVD
-*  procedure is achieved if used in an  accelerated version of Drmac and
-*  Veselic [5,6], and it is the kernel routine in the SIGMA library [7].
-*  Some tunning parameters (marked with [TP]) are available for the
-*  implementer.
-*  The computational range for the nonzero singular values is the  machine
-*  number interval ( UNDERFLOW , OVERFLOW ). In extreme cases, even
-*  denormalized singular values can be computed with the corresponding
-*  gradual loss of accurate digits.
-*
-*  Contributors
-*  ~~~~~~~~~~~~
-*  Zlatko Drmac (Zagreb, Croatia) and Kresimir Veselic (Hagen, Germany)
-*
-*  References
-*  ~~~~~~~~~~
-* [1] A. A. Anda and H. Park: Fast plane rotations with dynamic scaling.
-*     SIAM J. matrix Anal. Appl., Vol. 15 (1994), pp. 162-174.
-* [2] P. P. M. De Rijk: A one-sided Jacobi algorithm for computing the
-*     singular value decomposition on a vector computer.
-*     SIAM J. Sci. Stat. Comp., Vol. 10 (1998), pp. 359-371.
-* [3] J. Demmel and K. Veselic: Jacobi method is more accurate than QR.
-* [4] Z. Drmac: Implementation of Jacobi rotations for accurate singular
-*     value computation in floating point arithmetic.
-*     SIAM J. Sci. Comp., Vol. 18 (1997), pp. 1200-1222.
-* [5] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm I.
-*     SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1322-1342.
-*     LAPACK Working note 169.
-* [6] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm II.
-*     SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1343-1362.
-*     LAPACK Working note 170.
-* [7] Z. Drmac: SIGMA - mathematical software library for accurate SVD, PSV,
-*     QSVD, (H,K)-SVD computations.
-*     Department of Mathematics, University of Zagreb, 2008.
-*
-*  Bugs, Examples and Comments
-*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~
-*  Please report all bugs and send interesting test examples and comments to
-*  drmac@math.hr. Thank you.
-*
 *  Arguments
 *  =========
 *
@@ -251,6 +200,60 @@
 *                of sweeps. The output may still be useful. See the
 *                description of WORK.
 *
+*  Further Details
+*  ===============
+*
+*  The orthogonal N-by-N matrix V is obtained as a product of Jacobi plane
+*  rotations. The rotations are implemented as fast scaled rotations of
+*  Anda and Park [1]. In the case of underflow of the Jacobi angle, a
+*  modified Jacobi transformation of Drmac [4] is used. Pivot strategy uses
+*  column interchanges of de Rijk [2]. The relative accuracy of the computed
+*  singular values and the accuracy of the computed singular vectors (in
+*  angle metric) is as guaranteed by the theory of Demmel and Veselic [3].
+*  The condition number that determines the accuracy in the full rank case
+*  is essentially min_{D=diag} kappa(A*D), where kappa(.) is the
+*  spectral condition number. The best performance of this Jacobi SVD
+*  procedure is achieved if used in an  accelerated version of Drmac and
+*  Veselic [5,6], and it is the kernel routine in the SIGMA library [7].
+*  Some tunning parameters (marked with [TP]) are available for the
+*  implementer.
+*  The computational range for the nonzero singular values is the  machine
+*  number interval ( UNDERFLOW , OVERFLOW ). In extreme cases, even
+*  denormalized singular values can be computed with the corresponding
+*  gradual loss of accurate digits.
+*
+*  Contributors
+*  ============
+*
+*  Zlatko Drmac (Zagreb, Croatia) and Kresimir Veselic (Hagen, Germany)
+*
+*  References
+*  ==========
+*
+* [1] A. A. Anda and H. Park: Fast plane rotations with dynamic scaling.
+*     SIAM J. matrix Anal. Appl., Vol. 15 (1994), pp. 162-174.
+* [2] P. P. M. De Rijk: A one-sided Jacobi algorithm for computing the
+*     singular value decomposition on a vector computer.
+*     SIAM J. Sci. Stat. Comp., Vol. 10 (1998), pp. 359-371.
+* [3] J. Demmel and K. Veselic: Jacobi method is more accurate than QR.
+* [4] Z. Drmac: Implementation of Jacobi rotations for accurate singular
+*     value computation in floating point arithmetic.
+*     SIAM J. Sci. Comp., Vol. 18 (1997), pp. 1200-1222.
+* [5] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm I.
+*     SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1322-1342.
+*     LAPACK Working note 169.
+* [6] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm II.
+*     SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1343-1362.
+*     LAPACK Working note 170.
+* [7] Z. Drmac: SIGMA - mathematical software library for accurate SVD, PSV,
+*     QSVD, (H,K)-SVD computations.
+*     Department of Mathematics, University of Zagreb, 2008.
+*
+*  Bugs, Examples and Comments
+*  ===========================
+*  Please report all bugs and send interesting test examples and comments to
+*  drmac@math.hr. Thank you.
+*
 *  =====================================================================
 *
 *     .. Local Parameters ..