SUBROUTINE CSPSVX( FACT, UPLO, N, NRHS, AP, AFP, IPIV, B, LDB, X,
$ LDX, RCOND, FERR, BERR, WORK, RWORK, INFO )
*
* -- LAPACK driver routine (version 3.1) --
* 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( * )
REAL BERR( * ), FERR( * ), RWORK( * )
COMPLEX AFP( * ), AP( * ), B( LDB, * ), WORK( * ),
$ X( LDX, * )
* ..
*
* Purpose
* =======
*
* CSPSVX uses the diagonal pivoting factorization A = U*D*U**T or
* A = L*D*L**T to compute the solution to a complex 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) COMPLEX 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) COMPLEX 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 CSPTRF, 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 CSPTRF, 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 CSPTRF.
* 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 CSPTRF.
*
* B (input) COMPLEX 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) COMPLEX 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) COMPLEX array, dimension (2*N)
*
* RWORK (workspace) REAL 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 CLANSP, SLAMCH
EXTERNAL LSAME, CLANSP, SLAMCH
* ..
* .. External Subroutines ..
EXTERNAL CCOPY, CLACPY, CSPCON, CSPRFS, CSPTRF, CSPTRS,
$ 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( 'CSPSVX', -INFO )
RETURN
END IF
*
IF( NOFACT ) THEN
*
* Compute the factorization A = U*D*U' or A = L*D*L'.
*
CALL CCOPY( N*( N+1 ) / 2, AP, 1, AFP, 1 )
CALL CSPTRF( 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 = CLANSP( 'I', UPLO, N, AP, RWORK )
*
* Compute the reciprocal of the condition number of A.
*
CALL CSPCON( UPLO, N, AFP, IPIV, ANORM, RCOND, WORK, INFO )
*
* Compute the solution vectors X.
*
CALL CLACPY( 'Full', N, NRHS, B, LDB, X, LDX )
CALL CSPTRS( 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 CSPRFS( UPLO, N, NRHS, AP, AFP, IPIV, B, LDB, X, LDX, FERR,
$ BERR, WORK, RWORK, INFO )
*
* Set INFO = N+1 if the matrix is singular to working precision.
*
IF( RCOND.LT.SLAMCH( 'Epsilon' ) )
$ INFO = N + 1
*
RETURN
*
* End of CSPSVX
*
END