subroutine slatsqr (M, N, MB, NB, A, LDA, T, LDT, WORK, LWORK, INFO)
subroutine slatsqr (integer M, integer N, integer MB, integer NB, real, dimension( lda, * ) A, integer LDA, real, dimension(ldt, *) T, integer LDT, real, dimension( * ) WORK, integer LWORK, integer INFO)
SLATSQR computes a blocked Tall-Skinny QR factorization of a real M-by-N matrix A for M >= N: A = Q * ( R ), ( 0 ) where: Q is a M-by-M orthogonal matrix, stored on exit in an implicit form in the elements below the diagonal of the array A and in the elements of the array T; R is an upper-triangular N-by-N matrix, stored on exit in the elements on and above the diagonal of the array A. 0 is a (M-N)-by-N zero matrix, and is not stored.
M is INTEGER The number of rows of the matrix A. M >= 0.
N is INTEGER The number of columns of the matrix A. M >= N >= 0.
MB is INTEGER The row block size to be used in the blocked QR. MB > N.
NB is INTEGER The column block size to be used in the blocked QR. N >= NB >= 1.
A is REAL array, dimension (LDA,N) On entry, the M-by-N matrix A. On exit, the elements on and above the diagonal of the array contain the N-by-N upper triangular matrix R; the elements below the diagonal represent Q by the columns of blocked V (see Further Details).
LDA is INTEGER The leading dimension of the array A. LDA >= max(1,M).
T is REAL array, dimension (LDT, N * Number_of_row_blocks) where Number_of_row_blocks = CEIL((M-N)/(MB-N)) The blocked upper triangular block reflectors stored in compact form as a sequence of upper triangular blocks. See Further Details below.
LDT is INTEGER The leading dimension of the array T. LDT >= NB.
(workspace) REAL array, dimension (MAX(1,LWORK))
The dimension of the array WORK. LWORK >= NB*N. 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.
INFO is INTEGER = 0: successful exit < 0: if INFO = -i, the i-th argument had an illegal value
Univ. of Tennessee
Univ. of California Berkeley
Univ. of Colorado Denver
Tall-Skinny QR (TSQR) performs QR by a sequence of orthogonal transformations, representing Q as a product of other orthogonal matrices Q = Q(1) * Q(2) * . . . * Q(k) where each Q(i) zeros out subdiagonal entries of a block of MB rows of A: Q(1) zeros out the subdiagonal entries of rows 1:MB of A Q(2) zeros out the bottom MB-N rows of rows [1:N,MB+1:2*MB-N] of A Q(3) zeros out the bottom MB-N rows of rows [1:N,2*MB-N+1:3*MB-2*N] of A . . . Q(1) is computed by GEQRT, which represents Q(1) by Householder vectors stored under the diagonal of rows 1:MB of A, and by upper triangular block reflectors, stored in array T(1:LDT,1:N). For more information see Further Details in GEQRT. Q(i) for i>1 is computed by TPQRT, which represents Q(i) by Householder vectors stored in rows [(i-1)*(MB-N)+N+1:i*(MB-N)+N] of A, and by upper triangular block reflectors, stored in array T(1:LDT,(i-1)*N+1:i*N). The last Q(k) may use fewer rows. For more information see Further Details in TPQRT. For more details of the overall algorithm, see the description of Sequential TSQR in Section 2.2 of .  “Communication-Optimal Parallel and Sequential QR and LU Factorizations,” J. Demmel, L. Grigori, M. Hoemmen, J. Langou, SIAM J. Sci. Comput, vol. 34, no. 1, 2012
Definition at line 164 of file slatsqr.f.
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The man page slatsqr(3) is an alias of slatsqr.f(3).