      SUBROUTINE SSPSVX( FACT, UPLO, N, NRHS, AP, AFP, IPIV, B, LDB, X,
     $                   LDX, RCOND, FERR, BERR, WORK, IWORK, INFO )
*
*  -- LAPACK driver routine (version 3.2) --
*     Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd..
*     November 2006
*
*     .. Scalar Arguments ..
      CHARACTER          FACT, UPLO
      INTEGER            INFO, LDB, LDX, N, NRHS
      REAL               RCOND
*     ..
*     .. Array Arguments ..
      INTEGER            IPIV( * ), IWORK( * )
      REAL               AFP( * ), AP( * ), B( LDB, * ), BERR( * ),
     $                   FERR( * ), WORK( * ), X( LDX, * )
*     ..
*
*  Purpose
*  =======
*
*  SSPSVX uses the diagonal pivoting factorization A = U*D*U**T or
*  A = L*D*L**T to compute the solution to a real system of linear
*  equations A * X = B, where A is an N-by-N symmetric matrix stored
*  in packed format and X and B are N-by-NRHS matrices.
*
*  Error bounds on the solution and a condition estimate are also
*  provided.
*
*  Description
*  ===========
*
*  The following steps are performed:
*
*  1. If FACT = 'N', the diagonal pivoting method is used to factor A as
*        A = U * D * U**T,  if UPLO = 'U', or
*        A = L * D * L**T,  if UPLO = 'L',
*     where U (or L) is a product of permutation and unit upper (lower)
*     triangular matrices and D is symmetric and block diagonal with
*     1-by-1 and 2-by-2 diagonal blocks.
*
*  2. If some D(i,i)=0, so that D is exactly singular, then the routine
*     returns with INFO = i. Otherwise, the factored form of A is used
*     to estimate the condition number of the matrix A.  If the
*     reciprocal of the condition number is less than machine precision,
*     INFO = N+1 is returned as a warning, but the routine still goes on
*     to solve for X and compute error bounds as described below.
*
*  3. The system of equations is solved for X using the factored form
*     of A.
*
*  4. Iterative refinement is applied to improve the computed solution
*     matrix and calculate error bounds and backward error estimates
*     for it.
*
*  Arguments
*  =========
*
*  FACT    (input) CHARACTER*1
*          Specifies whether or not the factored form of A has been
*          supplied on entry.
*          = 'F':  On entry, AFP and IPIV contain the factored form of
*                  A.  AP, AFP and IPIV will not be modified.
*          = 'N':  The matrix A will be copied to AFP and factored.
*
*  UPLO    (input) CHARACTER*1
*          = 'U':  Upper triangle of A is stored;
*          = 'L':  Lower triangle of A is stored.
*
*  N       (input) INTEGER
*          The number of linear equations, i.e., the order of the
*          matrix A.  N >= 0.
*
*  NRHS    (input) INTEGER
*          The number of right hand sides, i.e., the number of columns
*          of the matrices B and X.  NRHS >= 0.
*
*  AP      (input) REAL array, dimension (N*(N+1)/2)
*          The upper or lower triangle of the symmetric matrix A, packed
*          columnwise in a linear array.  The j-th column of A is stored
*          in the array AP as follows:
*          if UPLO = 'U', AP(i + (j-1)*j/2) = A(i,j) for 1<=i<=j;
*          if UPLO = 'L', AP(i + (j-1)*(2*n-j)/2) = A(i,j) for j<=i<=n.
*          See below for further details.
*
*  AFP     (input or output) REAL array, dimension
*                            (N*(N+1)/2)
*          If FACT = 'F', then AFP is an input argument and on entry
*          contains the block diagonal matrix D and the multipliers used
*          to obtain the factor U or L from the factorization
*          A = U*D*U**T or A = L*D*L**T as computed by SSPTRF, stored as
*          a packed triangular matrix in the same storage format as A.
*
*          If FACT = 'N', then AFP is an output argument and on exit
*          contains the block diagonal matrix D and the multipliers used
*          to obtain the factor U or L from the factorization
*          A = U*D*U**T or A = L*D*L**T as computed by SSPTRF, stored as
*          a packed triangular matrix in the same storage format as A.
*
*  IPIV    (input or output) INTEGER array, dimension (N)
*          If FACT = 'F', then IPIV is an input argument and on entry
*          contains details of the interchanges and the block structure
*          of D, as determined by SSPTRF.
*          If IPIV(k) > 0, then rows and columns k and IPIV(k) were
*          interchanged and D(k,k) is a 1-by-1 diagonal block.
*          If UPLO = 'U' and IPIV(k) = IPIV(k-1) < 0, then rows and
*          columns k-1 and -IPIV(k) were interchanged and D(k-1:k,k-1:k)
*          is a 2-by-2 diagonal block.  If UPLO = 'L' and IPIV(k) =
*          IPIV(k+1) < 0, then rows and columns k+1 and -IPIV(k) were
*          interchanged and D(k:k+1,k:k+1) is a 2-by-2 diagonal block.
*
*          If FACT = 'N', then IPIV is an output argument and on exit
*          contains details of the interchanges and the block structure
*          of D, as determined by SSPTRF.
*
*  B       (input) REAL array, dimension (LDB,NRHS)
*          The N-by-NRHS right hand side matrix B.
*
*  LDB     (input) INTEGER
*          The leading dimension of the array B.  LDB >= max(1,N).
*
*  X       (output) REAL array, dimension (LDX,NRHS)
*          If INFO = 0 or INFO = N+1, the N-by-NRHS solution matrix X.
*
*  LDX     (input) INTEGER
*          The leading dimension of the array X.  LDX >= max(1,N).
*
*  RCOND   (output) REAL
*          The estimate of the reciprocal condition number of the matrix
*          A.  If RCOND is less than the machine precision (in
*          particular, if RCOND = 0), the matrix is singular to working
*          precision.  This condition is indicated by a return code of
*          INFO > 0.
*
*  FERR    (output) REAL array, dimension (NRHS)
*          The estimated forward error bound for each solution vector
*          X(j) (the j-th column of the solution matrix X).
*          If XTRUE is the true solution corresponding to X(j), FERR(j)
*          is an estimated upper bound for the magnitude of the largest
*          element in (X(j) - XTRUE) divided by the magnitude of the
*          largest element in X(j).  The estimate is as reliable as
*          the estimate for RCOND, and is almost always a slight
*          overestimate of the true error.
*
*  BERR    (output) REAL array, dimension (NRHS)
*          The componentwise relative backward error of each solution
*          vector X(j) (i.e., the smallest relative change in
*          any element of A or B that makes X(j) an exact solution).
*
*  WORK    (workspace) REAL array, dimension (3*N)
*
*  IWORK   (workspace) INTEGER array, dimension (N)
*
*  INFO    (output) INTEGER
*          = 0: successful exit
*          < 0: if INFO = -i, the i-th argument had an illegal value
*          > 0:  if INFO = i, and i is
*                <= N:  D(i,i) is exactly zero.  The factorization
*                       has been completed but the factor D is exactly
*                       singular, so the solution and error bounds could
*                       not be computed. RCOND = 0 is returned.
*                = N+1: D is nonsingular, but RCOND is less than machine
*                       precision, meaning that the matrix is singular
*                       to working precision.  Nevertheless, the
*                       solution and error bounds are computed because
*                       there are a number of situations where the
*                       computed solution can be more accurate than the
*                       value of RCOND would suggest.
*
*  Further Details
*  ===============
*
*  The packed storage scheme is illustrated by the following example
*  when N = 4, UPLO = 'U':
*
*  Two-dimensional storage of the symmetric matrix A:
*
*     a11 a12 a13 a14
*         a22 a23 a24
*             a33 a34     (aij = aji)
*                 a44
*
*  Packed storage of the upper triangle of A:
*
*  AP = [ a11, a12, a22, a13, a23, a33, a14, a24, a34, a44 ]
*
*  =====================================================================
*
*     .. Parameters ..
      REAL               ZERO
      PARAMETER          ( ZERO = 0.0E+0 )
*     ..
*     .. Local Scalars ..
      LOGICAL            NOFACT
      REAL               ANORM
*     ..
*     .. External Functions ..
      LOGICAL            LSAME
      REAL               SLAMCH, SLANSP
      EXTERNAL           LSAME, SLAMCH, SLANSP
*     ..
*     .. External Subroutines ..
      EXTERNAL           SCOPY, SLACPY, SSPCON, SSPRFS, SSPTRF, SSPTRS,
     $                   XERBLA
*     ..
*     .. Intrinsic Functions ..
      INTRINSIC          MAX
*     ..
*     .. Executable Statements ..
*
*     Test the input parameters.
*
      INFO = 0
      NOFACT = LSAME( FACT, 'N' )
      IF( .NOT.NOFACT .AND. .NOT.LSAME( FACT, 'F' ) ) THEN
         INFO = -1
      ELSE IF( .NOT.LSAME( UPLO, 'U' ) .AND. .NOT.LSAME( UPLO, 'L' ) )
     $          THEN
         INFO = -2
      ELSE IF( N.LT.0 ) THEN
         INFO = -3
      ELSE IF( NRHS.LT.0 ) THEN
         INFO = -4
      ELSE IF( LDB.LT.MAX( 1, N ) ) THEN
         INFO = -9
      ELSE IF( LDX.LT.MAX( 1, N ) ) THEN
         INFO = -11
      END IF
      IF( INFO.NE.0 ) THEN
         CALL XERBLA( 'SSPSVX', -INFO )
         RETURN
      END IF
*
      IF( NOFACT ) THEN
*
*        Compute the factorization A = U*D*U' or A = L*D*L'.
*
         CALL SCOPY( N*( N+1 ) / 2, AP, 1, AFP, 1 )
         CALL SSPTRF( UPLO, N, AFP, IPIV, INFO )
*
*        Return if INFO is non-zero.
*
         IF( INFO.GT.0 )THEN
            RCOND = ZERO
            RETURN
         END IF
      END IF
*
*     Compute the norm of the matrix A.
*
      ANORM = SLANSP( 'I', UPLO, N, AP, WORK )
*
*     Compute the reciprocal of the condition number of A.
*
      CALL SSPCON( UPLO, N, AFP, IPIV, ANORM, RCOND, WORK, IWORK, INFO )
*
*     Compute the solution vectors X.
*
      CALL SLACPY( 'Full', N, NRHS, B, LDB, X, LDX )
      CALL SSPTRS( UPLO, N, NRHS, AFP, IPIV, X, LDX, INFO )
*
*     Use iterative refinement to improve the computed solutions and
*     compute error bounds and backward error estimates for them.
*
      CALL SSPRFS( UPLO, N, NRHS, AP, AFP, IPIV, B, LDB, X, LDX, FERR,
     $             BERR, WORK, IWORK, INFO )
*
*     Set INFO = N+1 if the matrix is singular to working precision.
*
      IF( RCOND.LT.SLAMCH( 'Epsilon' ) )
     $   INFO = N + 1
*
      RETURN
*
*     End of SSPSVX
*
      END
