SRC/sgeqp3rk.f(3)

NAME
SRC/sgeqp3rk.f

SYNOPSIS

  Functions/Subroutines
subroutine sgeqp3rk (m, n, nrhs, kmax, abstol, reltol, a, lda, k, maxc2nrmk, relmaxc2nrmk, jpiv, tau, work, lwork, iwork, info)
SGEQP3RK computes a truncated Householder QR factorization with column pivoting of a real m-by-n matrix A by using Level 3 BLAS and overwrites a real m-by-nrhs matrix B with Q**T * B.

Function/Subroutine Documentation

  subroutine sgeqp3rk (integer m, integer n, integer nrhs, integer kmax, real abstol, real reltol, real, dimension( lda, * ) a, integer lda, integer k, real maxc2nrmk, real relmaxc2nrmk, integer, dimension( * ) jpiv, real, dimension( * ) tau, real, dimension( * ) work, integer lwork, integer, dimension( * ) iwork, integer info)

SGEQP3RK computes a truncated Householder QR factorization with column pivoting of a real m-by-n matrix A by using Level 3 BLAS and overwrites a real m-by-nrhs matrix B with Q**T * B.

Purpose:

     SGEQP3RK performs two tasks simultaneously:
     Task 1: The routine computes a truncated (rank K) or full rank
     Householder QR factorization with column pivoting of a real
     M-by-N matrix A using Level 3 BLAS. K is the number of columns
     that were factorized, i.e. factorization rank of the
     factor R, K <= min(M,N).
      A * P(K) = Q(K) * R(K)  =
            = Q(K) * ( R11(K) R12(K) ) = Q(K) * (   R(K)_approx    )
                     ( 0      R22(K) )          ( 0  R(K)_residual ),
     where:
      P(K)            is an N-by-N permutation matrix;
      Q(K)            is an M-by-M orthogonal matrix;
      R(K)_approx   = ( R11(K), R12(K) ) is a rank K approximation of the
                        full rank factor R with K-by-K upper-triangular
                        R11(K) and K-by-N rectangular R12(K). The diagonal
                        entries of R11(K) appear in non-increasing order
                        of absolute value, and absolute values of all of
                        them exceed the maximum column 2-norm of R22(K)
                        up to roundoff error.
      R(K)_residual = R22(K) is the residual of a rank K approximation
                        of the full rank factor R. It is a
                        an (M-K)-by-(N-K) rectangular matrix;
      0               is a an (M-K)-by-K zero matrix.
     Task 2: At the same time, the routine overwrites a real M-by-NRHS
     matrix B with  Q(K)**T * B  using Level 3 BLAS.
     =====================================================================
     The matrices A and B are stored on input in the array A as
     the left and right blocks A(1:M,1:N) and A(1:M, N+1:N+NRHS)
     respectively.
                                      N     NRHS
                 array_A   =   M  [ mat_A, mat_B ]
     The truncation criteria (i.e. when to stop the factorization)
     can be any of the following:
       1) The input parameter KMAX, the maximum number of columns
          KMAX to factorize, i.e. the factorization rank is limited
          to KMAX. If KMAX >= min(M,N), the criterion is not used.
       2) The input parameter ABSTOL, the absolute tolerance for
          the maximum column 2-norm of the residual matrix R22(K). This
          means that the factorization stops if this norm is less or
          equal to ABSTOL. If ABSTOL < 0.0, the criterion is not used.
       3) The input parameter RELTOL, the tolerance for the maximum
          column 2-norm matrix of the residual matrix R22(K) divided
          by the maximum column 2-norm of the original matrix A, which
          is equal to abs(R(1,1)). This means that the factorization stops
          when the ratio of the maximum column 2-norm of R22(K) to
          the maximum column 2-norm of A is less than or equal to RELTOL.
          If RELTOL < 0.0, the criterion is not used.
       4) In case both stopping criteria ABSTOL or RELTOL are not used,
          and when the residual matrix R22(K) is a zero matrix in some
          factorization step K. ( This stopping criterion is implicit. )
      The algorithm stops when any of these conditions is first
      satisfied, otherwise the whole matrix A is factorized.
      To factorize the whole matrix A, use the values
      KMAX >= min(M,N), ABSTOL < 0.0 and RELTOL < 0.0.
      The routine returns:
         a) Q(K), R(K)_approx = ( R11(K), R12(K) ),
            R(K)_residual = R22(K), P(K), i.e. the resulting matrices
            of the factorization; P(K) is represented by JPIV,
            ( if K = min(M,N), R(K)_approx is the full factor R,
            and there is no residual matrix R(K)_residual);
         b) K, the number of columns that were factorized,
            i.e. factorization rank;
         c) MAXC2NRMK, the maximum column 2-norm of the residual
            matrix R(K)_residual = R22(K),
            ( if K = min(M,N), MAXC2NRMK = 0.0 );
         d) RELMAXC2NRMK equals MAXC2NRMK divided by MAXC2NRM, the maximum
            column 2-norm of the original matrix A, which is equal
            to abs(R(1,1)), ( if K = min(M,N), RELMAXC2NRMK = 0.0 );
         e) Q(K)**T * B, the matrix B with the orthogonal
            transformation Q(K)**T applied on the left.
     The N-by-N permutation matrix P(K) is stored in a compact form in
     the integer array JPIV. For 1 <= j <= N, column j
     of the matrix A was interchanged with column JPIV(j).
     The M-by-M orthogonal matrix Q is represented as a product
     of elementary Householder reflectors
         Q(K) = H(1) *  H(2) * . . . * H(K),
     where K is the number of columns that were factorized.
     Each H(j) has the form
         H(j) = I - tau * v * v**T,
     where 1 <= j <= K and
       I    is an M-by-M identity matrix,
       tau  is a real scalar,
       v    is a real vector with v(1:j-1) = 0 and v(j) = 1.
     v(j+1:M) is stored on exit in A(j+1:M,j) and tau in TAU(j).
     See the Further Details section for more information.

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. N >= 0.

    NRHS

              NRHS is INTEGER
              The number of right hand sides, i.e. the number of
              columns of the matrix B. NRHS >= 0.

    KMAX

              KMAX is INTEGER
              The first factorization stopping criterion. KMAX >= 0.
              The maximum number of columns of the matrix A to factorize,
              i.e. the maximum factorization rank.
              a) If KMAX >= min(M,N), then this stopping criterion
                    is not used, the routine factorizes columns
                    depending on ABSTOL and RELTOL.
              b) If KMAX = 0, then this stopping criterion is
                    satisfied on input and the routine exits immediately.
                    This means that the factorization is not performed,
                    the matrices A and B are not modified, and
                    the matrix A is itself the residual.

    ABSTOL

              ABSTOL is REAL
              The second factorization stopping criterion, cannot be NaN.
              The absolute tolerance (stopping threshold) for
              maximum column 2-norm of the residual matrix R22(K).
              The algorithm converges (stops the factorization) when
              the maximum column 2-norm of the residual matrix R22(K)
              is less than or equal to ABSTOL. Let SAFMIN = SLAMCH('S').
              a) If ABSTOL is NaN, then no computation is performed
                    and an error message ( INFO = -5 ) is issued
                    by XERBLA.
              b) If ABSTOL < 0.0, then this stopping criterion is not
                    used, the routine factorizes columns depending
                    on KMAX and RELTOL.
                    This includes the case ABSTOL = -Inf.
              c) If 0.0 <= ABSTOL < 2*SAFMIN, then ABSTOL = 2*SAFMIN
                    is used. This includes the case ABSTOL = -0.0.
              d) If 2*SAFMIN <= ABSTOL then the input value
                    of ABSTOL is used.
              Let MAXC2NRM be the maximum column 2-norm of the
              whole original matrix A.
              If ABSTOL chosen above is >= MAXC2NRM, then this
              stopping criterion is satisfied on input and routine exits
              immediately after MAXC2NRM is computed. The routine
              returns MAXC2NRM in MAXC2NORMK,
              and 1.0 in RELMAXC2NORMK.
              This includes the case ABSTOL = +Inf. This means that the
              factorization is not performed, the matrices A and B are not
              modified, and the matrix A is itself the residual.

    RELTOL

              RELTOL is REAL
              The third factorization stopping criterion, cannot be NaN.
              The tolerance (stopping threshold) for the ratio
              abs(R(K+1,K+1))/abs(R(1,1)) of the maximum column 2-norm of
              the residual matrix R22(K) to the maximum column 2-norm of
              the original matrix A. The algorithm converges (stops the
              factorization), when abs(R(K+1,K+1))/abs(R(1,1)) A is less
              than or equal to RELTOL. Let EPS = SLAMCH('E').
              a) If RELTOL is NaN, then no computation is performed
                    and an error message ( INFO = -6 ) is issued
                    by XERBLA.
              b) If RELTOL < 0.0, then this stopping criterion is not
                    used, the routine factorizes columns depending
                    on KMAX and ABSTOL.
                    This includes the case RELTOL = -Inf.
              c) If 0.0 <= RELTOL < EPS, then RELTOL = EPS is used.
                    This includes the case RELTOL = -0.0.
              d) If EPS <= RELTOL then the input value of RELTOL
                    is used.
              Let MAXC2NRM be the maximum column 2-norm of the
              whole original matrix A.
              If RELTOL chosen above is >= 1.0, then this stopping
              criterion is satisfied on input and routine exits
              immediately after MAXC2NRM is computed.
              The routine returns MAXC2NRM in MAXC2NORMK,
              and 1.0 in RELMAXC2NORMK.
              This includes the case RELTOL = +Inf. This means that the
              factorization is not performed, the matrices A and B are not
              modified, and the matrix A is itself the residual.
              NOTE: We recommend that RELTOL satisfy
                    min( max(M,N)*EPS, sqrt(EPS) ) <= RELTOL

    A

              A is REAL array, dimension (LDA,N+NRHS)
              On entry:
              a) The subarray A(1:M,1:N) contains the M-by-N matrix A.
              b) The subarray A(1:M,N+1:N+NRHS) contains the M-by-NRHS
                 matrix B.
                                      N     NRHS
                  array_A   =   M  [ mat_A, mat_B ]
              On exit:
              a) The subarray A(1:M,1:N) contains parts of the factors
                 of the matrix A:
                1) If K = 0, A(1:M,1:N) contains the original matrix A.
                2) If K > 0, A(1:M,1:N) contains parts of the
                factors:
                  1. The elements below the diagonal of the subarray
                     A(1:M,1:K) together with TAU(1:K) represent the
                     orthogonal matrix Q(K) as a product of K Householder
                     elementary reflectors.
                  2. The elements on and above the diagonal of
                     the subarray A(1:K,1:N) contain K-by-N
                     upper-trapezoidal matrix
                     R(K)_approx = ( R11(K), R12(K) ).
                     NOTE: If K=min(M,N), i.e. full rank factorization,
                           then R_approx(K) is the full factor R which
                           is upper-trapezoidal. If, in addition, M>=N,
                           then R is upper-triangular.
                  3. The subarray A(K+1:M,K+1:N) contains (M-K)-by-(N-K)
                     rectangular matrix R(K)_residual = R22(K).
              b) If NRHS > 0, the subarray A(1:M,N+1:N+NRHS) contains
                 the M-by-NRHS product Q(K)**T * B.

    LDA

              LDA is INTEGER
              The leading dimension of the array A. LDA >= max(1,M).
              This is the leading dimension for both matrices, A and B.

    K

              K is INTEGER
              Factorization rank of the matrix A, i.e. the rank of
              the factor R, which is the same as the number of non-zero
              rows of the factor R. 0 <= K <= min(M,KMAX,N).
              K also represents the number of non-zero Householder
              vectors.
              NOTE: If K = 0, a) the arrays A and B are not modified;
                              b) the array TAU(1:min(M,N)) is set to ZERO,
                                 if the matrix A does not contain NaN,
                                 otherwise the elements TAU(1:min(M,N))
                                 are undefined;
                              c) the elements of the array JPIV are set
                                 as follows: for j = 1:N, JPIV(j) = j.

    MAXC2NRMK

              MAXC2NRMK is REAL
              The maximum column 2-norm of the residual matrix R22(K),
              when the factorization stopped at rank K. MAXC2NRMK >= 0.
              a) If K = 0, i.e. the factorization was not performed,
                 the matrix A was not modified and is itself a residual
                 matrix, then MAXC2NRMK equals the maximum column 2-norm
                 of the original matrix A.
              b) If 0 < K < min(M,N), then MAXC2NRMK is returned.
              c) If K = min(M,N), i.e. the whole matrix A was
                 factorized and there is no residual matrix,
                 then MAXC2NRMK = 0.0.
              NOTE: MAXC2NRMK in the factorization step K would equal
                    R(K+1,K+1) in the next factorization step K+1.

    RELMAXC2NRMK

              RELMAXC2NRMK is REAL
              The ratio MAXC2NRMK / MAXC2NRM of the maximum column
              2-norm of the residual matrix R22(K) (when the factorization
              stopped at rank K) to the maximum column 2-norm of the
              whole original matrix A. RELMAXC2NRMK >= 0.
              a) If K = 0, i.e. the factorization was not performed,
                 the matrix A was not modified and is itself a residual
                 matrix, then RELMAXC2NRMK = 1.0.
              b) If 0 < K < min(M,N), then
                    RELMAXC2NRMK = MAXC2NRMK / MAXC2NRM is returned.
              c) If K = min(M,N), i.e. the whole matrix A was
                 factorized and there is no residual matrix,
                 then RELMAXC2NRMK = 0.0.
             NOTE: RELMAXC2NRMK in the factorization step K would equal
                   abs(R(K+1,K+1))/abs(R(1,1)) in the next factorization
                   step K+1.

    JPIV

              JPIV is INTEGER array, dimension (N)
              Column pivot indices. For 1 <= j <= N, column j
              of the matrix A was interchanged with column JPIV(j).
              The elements of the array JPIV(1:N) are always set
              by the routine, for example, even  when no columns
              were factorized, i.e. when K = 0, the elements are
              set as JPIV(j) = j for j = 1:N.

    TAU

              TAU is REAL array, dimension (min(M,N))
              The scalar factors of the elementary reflectors.
              If 0 < K <= min(M,N), only the elements TAU(1:K) of
              the array TAU are modified by the factorization.
              After the factorization computed, if no NaN was found
              during the factorization, the remaining elements
              TAU(K+1:min(M,N)) are set to zero, otherwise the
              elements TAU(K+1:min(M,N)) are not set and therefore
              undefined.
              ( If K = 0, all elements of TAU are set to zero, if
              the matrix A does not contain NaN. )

    WORK

              WORK is REAL array, dimension (MAX(1,LWORK))
              On exit, if INFO = 0, WORK(1) returns the optimal LWORK.

    LWORK

              LWORK is INTEGER
              The dimension of the array WORK.
    For optimal performance LWORK >= (2*N + NB*( N+NRHS+1 )),
              where NB is the optimal block size for SGEQP3RK returned
              by ILAENV. Minimal block size MINNB=2.
              NOTE: The decision, whether to use unblocked BLAS 2
              or blocked BLAS 3 code is based not only on the dimension
              LWORK of the availbale workspace WORK, but also also on the
              matrix A dimension N via crossover point NX returned
              by ILAENV. (For N less than NX, unblocked code should be
              used.)
              If LWORK = -1, then a workspace query is assumed;
              the routine only calculates the optimal size of the WORK
              array, returns this value as the first entry of the WORK
              array, and no error message related to LWORK is issued
              by XERBLA.

    IWORK

              IWORK is INTEGER array, dimension (N-1).
              Is a work array. ( IWORK is used to store indices
              of 'bad' columns for norm downdating in the residual
              matrix in the blocked step auxiliary subroutine SLAQP3RK ).

    INFO

              INFO is INTEGER
              1) INFO = 0: successful exit.
              2) INFO < 0: if INFO = -i, the i-th argument had an
                           illegal value.
              3) If INFO = j_1, where 1 <= j_1 <= N, then NaN was
                 detected and the routine stops the computation.
                 The j_1-th column of the matrix A or the j_1-th
                 element of array TAU contains the first occurrence
                 of NaN in the factorization step K+1 ( when K columns
                 have been factorized ).
                 On exit:
                 K                  is set to the number of
                                       factorized columns without
                                       exception.
                 MAXC2NRMK          is set to NaN.
                 RELMAXC2NRMK       is set to NaN.
                 TAU(K+1:min(M,N))  is not set and contains undefined
                                       elements. If j_1=K+1, TAU(K+1)
                                       may contain NaN.
              4) If INFO = j_2, where N+1 <= j_2 <= 2*N, then no NaN
                 was detected, but +Inf (or -Inf) was detected and
                 the routine continues the computation until completion.
                 The (j_2-N)-th column of the matrix A contains the first
                 occurrence of +Inf (or -Inf) in the factorization
                 step K+1 ( when K columns have been factorized ).

Author
    Univ. of Tennessee

    Univ. of California Berkeley

    Univ. of Colorado Denver

    NAG Ltd.

Further Details:

     SGEQP3RK is based on the same BLAS3 Householder QR factorization
     algorithm with column pivoting as in SGEQP3 routine which uses
     SLARFG routine to generate Householder reflectors
     for QR factorization.
     We can also write:
       A = A_approx(K) + A_residual(K)
     The low rank approximation matrix A(K)_approx from
     the truncated QR factorization of rank K of the matrix A is:
       A(K)_approx = Q(K) * ( R(K)_approx ) * P(K)**T
                            (     0     0 )
                   = Q(K) * ( R11(K) R12(K) ) * P(K)**T
                            (      0      0 )
     The residual A_residual(K) of the matrix A is:
       A_residual(K) = Q(K) * ( 0              0 ) * P(K)**T =
                              ( 0  R(K)_residual )
                     = Q(K) * ( 0        0 ) * P(K)**T
                              ( 0   R22(K) )
     The truncated (rank K) factorization guarantees that
     the maximum column 2-norm of A_residual(K) is less than
     or equal to MAXC2NRMK up to roundoff error.
     NOTE: An approximation of the null vectors
           of A can be easily computed from R11(K)
           and R12(K):
           Null( A(K) )_approx = P * ( inv(R11(K)) * R12(K) )
                                     (         -I           )

References:
    [1] A Level 3 BLAS QR factorization algorithm with column pivoting developed in 1996. G. Quintana-Orti, Depto. de Informatica, Universidad Jaime I, Spain. X. Sun, Computer Science Dept., Duke University, USA. C. H. Bischof, Math. and Comp. Sci. Div., Argonne National Lab, USA. A BLAS-3 version of the QR factorization with column pivoting. LAPACK Working Note 114 and in SIAM J. Sci. Comput., 19(5):1486-1494, Sept. 1998.

[2] A partial column norm updating strategy developed in 2006. Z. Drmac and Z. Bujanovic, Dept. of Math., University of Zagreb, Croatia. On the failure of rank revealing QR factorization software – a case study. LAPACK Working Note 176. and in ACM Trans. Math. Softw. 35, 2, Article 12 (July 2008), 28 pages.

Contributors:

      November  2023, Igor Kozachenko, James Demmel,
                      EECS Department,
                      University of California, Berkeley, USA.

Definition at line 582 of file sgeqp3rk.f.

Author

Generated automatically by Doxygen for LAPACK from the source code.