dorhr_col.f - Man Page

subroutine dorhr_col (m, n, nb, a, lda, t, ldt, d, info)
DORHR_COL

DORHR_COL  

Purpose:

  DORHR_COL takes an M-by-N real matrix Q_in with orthonormal columns
  as input, stored in A, and performs Householder Reconstruction (HR),
  i.e. reconstructs Householder vectors V(i) implicitly representing
  another M-by-N matrix Q_out, with the property that Q_in = Q_out*S,
  where S is an N-by-N diagonal matrix with diagonal entries
  equal to +1 or -1. The Householder vectors (columns V(i) of V) are
  stored in A on output, and the diagonal entries of S are stored in D.
  Block reflectors are also returned in T
  (same output format as DGEQRT).

Parameters

M

          M is INTEGER
          The number of rows of the matrix A. M >= 0.

N

          N is INTEGER
          The number of columns of the matrix A. M >= N >= 0.

NB

          NB is INTEGER
          The column block size to be used in the reconstruction
          of Householder column vector blocks in the array A and
          corresponding block reflectors in the array T. NB >= 1.
          (Note that if NB > N, then N is used instead of NB
          as the column block size.)

A

          A is DOUBLE PRECISION array, dimension (LDA,N)

          On entry:

             The array A contains an M-by-N orthonormal matrix Q_in,
             i.e the columns of A are orthogonal unit vectors.

          On exit:

             The elements below the diagonal of A represent the unit
             lower-trapezoidal matrix V of Householder column vectors
             V(i). The unit diagonal entries of V are not stored
             (same format as the output below the diagonal in A from
             DGEQRT). The matrix T and the matrix V stored on output
             in A implicitly define Q_out.

             The elements above the diagonal contain the factor U
             of the 'modified' LU-decomposition:
                Q_in - ( S ) = V * U
                       ( 0 )
             where 0 is a (M-N)-by-(M-N) zero matrix.

LDA

          LDA is INTEGER
          The leading dimension of the array A.  LDA >= max(1,M).

T

          T is DOUBLE PRECISION array,
          dimension (LDT, N)

          Let NOCB = Number_of_output_col_blocks
                   = CEIL(N/NB)

          On exit, T(1:NB, 1:N) contains NOCB upper-triangular
          block reflectors used to define Q_out stored in compact
          form as a sequence of upper-triangular NB-by-NB column
          blocks (same format as the output T in DGEQRT).
          The matrix T and the matrix V stored on output in A
          implicitly define Q_out. NOTE: The lower triangles
          below the upper-triangular blocks will be filled with
          zeros. See Further Details.

LDT

          LDT is INTEGER
          The leading dimension of the array T.
          LDT >= max(1,min(NB,N)).

D

          D is DOUBLE PRECISION array, dimension min(M,N).
          The elements can be only plus or minus one.

          D(i) is constructed as D(i) = -SIGN(Q_in_i(i,i)), where
          1 <= i <= min(M,N), and Q_in_i is Q_in after performing
          i-1 steps of “modified” Gaussian elimination.
          See Further Details.

INFO

          INFO is INTEGER
          = 0:  successful exit
          < 0:  if INFO = -i, the i-th argument had an illegal value

Further Details:

 The computed M-by-M orthogonal factor Q_out is defined implicitly as
 a product of orthogonal matrices Q_out(i). Each Q_out(i) is stored in
 the compact WY-representation format in the corresponding blocks of
 matrices V (stored in A) and T.

 The M-by-N unit lower-trapezoidal matrix V stored in the M-by-N
 matrix A contains the column vectors V(i) in NB-size column
 blocks VB(j). For example, VB(1) contains the columns
 V(1), V(2), ... V(NB). NOTE: The unit entries on
 the diagonal of Y are not stored in A.

 The number of column blocks is

     NOCB = Number_of_output_col_blocks = CEIL(N/NB)

 where each block is of order NB except for the last block, which
 is of order LAST_NB = N - (NOCB-1)*NB.

 For example, if M=6,  N=5 and NB=2, the matrix V is


     V = (    VB(1),   VB(2), VB(3) ) =

       = (   1                      )
         ( v21    1                 )
         ( v31  v32    1            )
         ( v41  v42  v43   1        )
         ( v51  v52  v53  v54    1  )
         ( v61  v62  v63  v54   v65 )


 For each of the column blocks VB(i), an upper-triangular block
 reflector TB(i) is computed. These blocks are stored as
 a sequence of upper-triangular column blocks in the NB-by-N
 matrix T. The size of each TB(i) block is NB-by-NB, except
 for the last block, whose size is LAST_NB-by-LAST_NB.

 For example, if M=6,  N=5 and NB=2, the matrix T is

     T  = (    TB(1),    TB(2), TB(3) ) =

        = ( t11  t12  t13  t14   t15  )
          (      t22       t24        )


 The M-by-M factor Q_out is given as a product of NOCB
 orthogonal M-by-M matrices Q_out(i).

     Q_out = Q_out(1) * Q_out(2) * ... * Q_out(NOCB),

 where each matrix Q_out(i) is given by the WY-representation
 using corresponding blocks from the matrices V and T:

     Q_out(i) = I - VB(i) * TB(i) * (VB(i))**T,

 where I is the identity matrix. Here is the formula with matrix
 dimensions:

  Q(i){M-by-M} = I{M-by-M} -
    VB(i){M-by-INB} * TB(i){INB-by-INB} * (VB(i))**T {INB-by-M},

 where INB = NB, except for the last block NOCB
 for which INB=LAST_NB.

 =====
 NOTE:
 =====

 If Q_in is the result of doing a QR factorization
 B = Q_in * R_in, then:

 B = (Q_out*S) * R_in = Q_out * (S * R_in) = Q_out * R_out.

 So if one wants to interpret Q_out as the result
 of the QR factorization of B, then the corresponding R_out
 should be equal to R_out = S * R_in, i.e. some rows of R_in
 should be multiplied by -1.

 For the details of the algorithm, see [1].

 [1] 'Reconstructing Householder vectors from tall-skinny QR',
     G. Ballard, J. Demmel, L. Grigori, M. Jacquelin, H.D. Nguyen,
     E. Solomonik, J. Parallel Distrib. Comput.,
     vol. 85, pp. 3-31, 2015.

Author

Univ. of Tennessee

Univ. of California Berkeley

Univ. of Colorado Denver

NAG Ltd.

Contributors:

 November   2019, Igor Kozachenko,
            Computer Science Division,
            University of California, Berkeley

Definition at line 258 of file dorhr_col.f.