/home/abui...unhr_col.f(3)

🎀 CUNHR_COL, bestie 🎀

okay so this is the <pink>Library Functions Manual</pink> page for /home/abui...unhr_col.f(3), and the actual source file is /home/abuild/rpmbuild/BUILD/lapack-static-3.12.0-build/lapack-3.12.0/SRC/cunhr_col.f. buckle up, she's a LAPACK girlie 💅

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🌸 NAME 🌸

/home/abuild/rpmbuild/BUILD/lapack-static-3.12.0-build/lapack-3.12.0/SRC/cunhr_col.f

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🌈 SYNOPSIS 🌈

Functions/Subroutines, periodt:

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

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🦋 Function/Subroutine Documentation 🦋

the full signature is giving main-character energy:

subroutine CUNHR_COL (integer m, integer n, integer nb, complex, dimension(
    lda, * ) a, integer lda, complex, dimension( ldt, * ) t, integer ldt,
    complex, dimension( * ) d, integer info)

and yes, she is literally named CUNHR_COL.

✨ Purpose ✨

okay so CUNHR_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 CGEQRT). 🫶

💖 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 bestie: if NB > N, then N is used instead of NB as the column block size. 💅

🌟 A

A is COMPLEX 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 CGEQRT). 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 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 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 CGEQRT). 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 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 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:
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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] ,
    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 cunhr_col.f.

👑 Author 👑

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