ZUNHR_COL takes an M-by-N complex 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 ZGEQRT).

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 COMPLEX*16 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


             ZGEQRT). 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 COMPLEX*16 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 ZGEQRT).


          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 COMPLEX*16 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

 The computed M-by-M unitary factor Q_out is defined implicitly as


 a product of unitary 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


 unitary 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.

Univ. of Tennessee

Univ. of California Berkeley

Univ. of Colorado Denver

NAG Ltd.

 November   2019, Igor Kozachenko,


            Computer Science Division,


            University of California, Berkeley