dlaein.f

来自「famous linear algebra library (LAPACK) p」· F 代码 · 共 532 行 · 第 1/2 页

      SUBROUTINE DLAEIN( RIGHTV, NOINIT, N, H, LDH, WR, WI, VR, VI, B,
     $                   LDB, WORK, EPS3, SMLNUM, BIGNUM, INFO )
*
*  -- LAPACK auxiliary routine (version 3.1) --
*     Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd..
*     November 2006
*
*     .. Scalar Arguments ..
      LOGICAL            NOINIT, RIGHTV
      INTEGER            INFO, LDB, LDH, N
      DOUBLE PRECISION   BIGNUM, EPS3, SMLNUM, WI, WR
*     ..
*     .. Array Arguments ..
      DOUBLE PRECISION   B( LDB, * ), H( LDH, * ), VI( * ), VR( * ),
     $                   WORK( * )
*     ..
*
*  Purpose
*  =======
*
*  DLAEIN uses inverse iteration to find a right or left eigenvector
*  corresponding to the eigenvalue (WR,WI) of a real upper Hessenberg
*  matrix H.
*
*  Arguments
*  =========
*
*  RIGHTV   (input) LOGICAL
*          = .TRUE. : compute right eigenvector;
*          = .FALSE.: compute left eigenvector.
*
*  NOINIT   (input) LOGICAL
*          = .TRUE. : no initial vector supplied in (VR,VI).
*          = .FALSE.: initial vector supplied in (VR,VI).
*
*  N       (input) INTEGER
*          The order of the matrix H.  N >= 0.
*
*  H       (input) DOUBLE PRECISION array, dimension (LDH,N)
*          The upper Hessenberg matrix H.
*
*  LDH     (input) INTEGER
*          The leading dimension of the array H.  LDH >= max(1,N).
*
*  WR      (input) DOUBLE PRECISION
*  WI      (input) DOUBLE PRECISION
*          The real and imaginary parts of the eigenvalue of H whose
*          corresponding right or left eigenvector is to be computed.
*
*  VR      (input/output) DOUBLE PRECISION array, dimension (N)
*  VI      (input/output) DOUBLE PRECISION array, dimension (N)
*          On entry, if NOINIT = .FALSE. and WI = 0.0, VR must contain
*          a real starting vector for inverse iteration using the real
*          eigenvalue WR; if NOINIT = .FALSE. and WI.ne.0.0, VR and VI
*          must contain the real and imaginary parts of a complex
*          starting vector for inverse iteration using the complex
*          eigenvalue (WR,WI); otherwise VR and VI need not be set.
*          On exit, if WI = 0.0 (real eigenvalue), VR contains the
*          computed real eigenvector; if WI.ne.0.0 (complex eigenvalue),
*          VR and VI contain the real and imaginary parts of the
*          computed complex eigenvector. The eigenvector is normalized
*          so that the component of largest magnitude has magnitude 1;
*          here the magnitude of a complex number (x,y) is taken to be
*          |x| + |y|.
*          VI is not referenced if WI = 0.0.
*
*  B       (workspace) DOUBLE PRECISION array, dimension (LDB,N)
*
*  LDB     (input) INTEGER
*          The leading dimension of the array B.  LDB >= N+1.
*
*  WORK   (workspace) DOUBLE PRECISION array, dimension (N)
*
*  EPS3    (input) DOUBLE PRECISION
*          A small machine-dependent value which is used to perturb
*          close eigenvalues, and to replace zero pivots.
*
*  SMLNUM  (input) DOUBLE PRECISION
*          A machine-dependent value close to the underflow threshold.
*
*  BIGNUM  (input) DOUBLE PRECISION
*          A machine-dependent value close to the overflow threshold.
*
*  INFO    (output) INTEGER
*          = 0:  successful exit
*          = 1:  inverse iteration did not converge; VR is set to the
*                last iterate, and so is VI if WI.ne.0.0.
*
*  =====================================================================
*
*     .. Parameters ..
      DOUBLE PRECISION   ZERO, ONE, TENTH
      PARAMETER          ( ZERO = 0.0D+0, ONE = 1.0D+0, TENTH = 1.0D-1 )
*     ..
*     .. Local Scalars ..
      CHARACTER          NORMIN, TRANS
      INTEGER            I, I1, I2, I3, IERR, ITS, J
      DOUBLE PRECISION   ABSBII, ABSBJJ, EI, EJ, GROWTO, NORM, NRMSML,
     $                   REC, ROOTN, SCALE, TEMP, VCRIT, VMAX, VNORM, W,
     $                   W1, X, XI, XR, Y
*     ..
*     .. External Functions ..
      INTEGER            IDAMAX
      DOUBLE PRECISION   DASUM, DLAPY2, DNRM2
      EXTERNAL           IDAMAX, DASUM, DLAPY2, DNRM2
*     ..
*     .. External Subroutines ..
      EXTERNAL           DLADIV, DLATRS, DSCAL
*     ..
*     .. Intrinsic Functions ..
      INTRINSIC          ABS, DBLE, MAX, SQRT
*     ..
*     .. Executable Statements ..
*
      INFO = 0
*
*     GROWTO is the threshold used in the acceptance test for an
*     eigenvector.
*
      ROOTN = SQRT( DBLE( N ) )
      GROWTO = TENTH / ROOTN
      NRMSML = MAX( ONE, EPS3*ROOTN )*SMLNUM
*
*     Form B = H - (WR,WI)*I (except that the subdiagonal elements and
*     the imaginary parts of the diagonal elements are not stored).
*
      DO 20 J = 1, N
         DO 10 I = 1, J - 1
            B( I, J ) = H( I, J )
   10    CONTINUE
         B( J, J ) = H( J, J ) - WR
   20 CONTINUE
*
      IF( WI.EQ.ZERO ) THEN
*
*        Real eigenvalue.
*
         IF( NOINIT ) THEN
*
*           Set initial vector.
*
            DO 30 I = 1, N
               VR( I ) = EPS3
   30       CONTINUE
         ELSE
*
*           Scale supplied initial vector.
*
            VNORM = DNRM2( N, VR, 1 )
            CALL DSCAL( N, ( EPS3*ROOTN ) / MAX( VNORM, NRMSML ), VR,
     $                  1 )
         END IF
*
         IF( RIGHTV ) THEN
*
*           LU decomposition with partial pivoting of B, replacing zero
*           pivots by EPS3.
*
            DO 60 I = 1, N - 1
               EI = H( I+1, I )
               IF( ABS( B( I, I ) ).LT.ABS( EI ) ) THEN
*
*                 Interchange rows and eliminate.
*
                  X = B( I, I ) / EI
                  B( I, I ) = EI
                  DO 40 J = I + 1, N
                     TEMP = B( I+1, J )
                     B( I+1, J ) = B( I, J ) - X*TEMP
                     B( I, J ) = TEMP
   40             CONTINUE
               ELSE
*
*                 Eliminate without interchange.
*
                  IF( B( I, I ).EQ.ZERO )
     $               B( I, I ) = EPS3
                  X = EI / B( I, I )
                  IF( X.NE.ZERO ) THEN
                     DO 50 J = I + 1, N
                        B( I+1, J ) = B( I+1, J ) - X*B( I, J )
   50                CONTINUE
                  END IF
               END IF
   60       CONTINUE
            IF( B( N, N ).EQ.ZERO )
     $         B( N, N ) = EPS3
*
            TRANS = 'N'
*
         ELSE
*
*           UL decomposition with partial pivoting of B, replacing zero
*           pivots by EPS3.
*
            DO 90 J = N, 2, -1
               EJ = H( J, J-1 )
               IF( ABS( B( J, J ) ).LT.ABS( EJ ) ) THEN
*
*                 Interchange columns and eliminate.
*
                  X = B( J, J ) / EJ
                  B( J, J ) = EJ
                  DO 70 I = 1, J - 1
                     TEMP = B( I, J-1 )
                     B( I, J-1 ) = B( I, J ) - X*TEMP
                     B( I, J ) = TEMP
   70             CONTINUE
               ELSE
*
*                 Eliminate without interchange.
*
                  IF( B( J, J ).EQ.ZERO )
     $               B( J, J ) = EPS3
                  X = EJ / B( J, J )
                  IF( X.NE.ZERO ) THEN
                     DO 80 I = 1, J - 1
                        B( I, J-1 ) = B( I, J-1 ) - X*B( I, J )
   80                CONTINUE
                  END IF
               END IF
   90       CONTINUE
            IF( B( 1, 1 ).EQ.ZERO )
     $         B( 1, 1 ) = EPS3
*
            TRANS = 'T'
*
         END IF
*
         NORMIN = 'N'
         DO 110 ITS = 1, N
*
*           Solve U*x = scale*v for a right eigenvector
*             or U'*x = scale*v for a left eigenvector,
*           overwriting x on v.
*
            CALL DLATRS( 'Upper', TRANS, 'Nonunit', NORMIN, N, B, LDB,
     $                   VR, SCALE, WORK, IERR )
            NORMIN = 'Y'
*
*           Test for sufficient growth in the norm of v.
*
            VNORM = DASUM( N, VR, 1 )
            IF( VNORM.GE.GROWTO*SCALE )
     $         GO TO 120
*
*           Choose new orthogonal starting vector and try again.
*
            TEMP = EPS3 / ( ROOTN+ONE )
            VR( 1 ) = EPS3
            DO 100 I = 2, N
               VR( I ) = TEMP
  100       CONTINUE
            VR( N-ITS+1 ) = VR( N-ITS+1 ) - EPS3*ROOTN
  110    CONTINUE
*
*        Failure to find eigenvector in N iterations.
*
         INFO = 1
*
  120    CONTINUE
*
*        Normalize eigenvector.
*
         I = IDAMAX( N, VR, 1 )
         CALL DSCAL( N, ONE / ABS( VR( I ) ), VR, 1 )