PDGELS(l)
β¨ PDGELS(l) β¨
okay bestie, welcome to PDGELS(l) β a LAPACK routine, version 1.5. This is the one that solves overdetermined or underdetermined real linear systems involving an M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1). itβs giving linear algebra main character energy π
πΈ NAME πΈ
PDGELS β solve overdetermined or underdetermined real linear systems involving an M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1),
π¦ SYNOPSIS π¦
SUBROUTINE PDGELS( TRANS, M, N, NRHS, A, IA, JA, DESCA, B, IB, JB,
DESCB, WORK, LWORK, INFO )
CHARACTER TRANS
INTEGER IA, IB, INFO, JA, JB, LWORK, M, N, NRHS
INTEGER DESCA( * ), DESCB( * )
DOUBLE PRECISION A( * ), B( * ), WORK( * )
π₯ PURPOSE π₯
No cap, PDGELS solves overdetermined or underdetermined real linear systems involving an M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1), or its transpose, using a QR or LQ factorization of sub( A ). It is assumed that sub( A ) has full rank. periodt π
The following options are provided:
- If
TRANS = 'N'andm >= n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem
minimize || sub( B ) - sub( A )*X ||.
- If
TRANS = 'N'andm < n: find the minimum norm solution of an underdetermined systemsub( A ) * X = sub( B ). - If
TRANS = 'T'andm >= n: find the minimum norm solution of an undetermined systemsub( A )**T * X = sub( B ). - If
TRANS = 'T'andm < n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem
minimize || sub( B ) - sub( A )**T * X ||.
where sub( B ) denotes B( IB:IB+M-1, JB:JB+NRHS-1 ) when TRANS = 'N' and B( IB:IB+N-1, JB:JB+NRHS-1 ) otherwise. Several right hand side vectors b and solution vectors x can be handled in a single call; When TRANS = 'N', the solution vectors are stored as the columns of the N-by-NRHS right hand side matrix sub( B ) and the M-by-NRHS right hand side matrix sub( B ) otherwise.
π Notes π
ok so, each global data object is described by an associated description vector. This vector stores the information required to establish the mapping between an object element and its corresponding process and memory location.
Let A be a generic term for any 2D block cyclicly distributed array. Such a global array has an associated description vector DESCA. In the following comments, the character _ should be read as "of the global array".
NOTATION β STORED IN β EXPLANATION
DTYPE_A(global)βDESCA( DTYPE_ )β The descriptor type. In this case,DTYPE_A = 1.CTXT_A (global)βDESCA( CTXT_ )β The BLACS context handle, indicating the BLACS process grid A is distributed over. The context itself is global, but the handle (the integer value) may vary.M_A (global)βDESCA( M_ )β The number of rows in the global array A.N_A (global)βDESCA( N_ )β The number of columns in the global array A.MB_A (global)βDESCA( MB_ )β The blocking factor used to distribute the rows of the array.NB_A (global)βDESCA( NB_ )β The blocking factor used to distribute the columns of the array.RSRC_A (global)βDESCA( RSRC_ )β The process row over which the first row of the array A is distributed.CSRC_A (global)βDESCA( CSRC_ )β The process column over which the first column of the array A is distributed.LLD_A (local)βDESCA( LLD_ )β The leading dimension of the local array.LLD_A >= MAX(1,LOCr(M_A)).
Let K be the number of rows or columns of a distributed matrix, and assume that its process grid has dimension p x q.
LOCr( K ) denotes the number of elements of K that a process would receive if K were distributed over the p processes of its process column.
Similarly, LOCc( K ) denotes the number of elements of K that a process would receive if K were distributed over the q processes of its process row.
The values of LOCr() and LOCc() may be determined via a call to the ScaLAPACK tool function, NUMROC:
LOCr( M ) = NUMROC( M, MB_A, MYROW, RSRC_A, NPROW ),
LOCc( N ) = NUMROC( N, NB_A, MYCOL, CSRC_A, NPCOL ).
An upper bound for these quantities may be computed by:
LOCr( M ) <= ceil( ceil(M/MB_A)/NPROW )*MB_A
LOCc( N ) <= ceil( ceil(N/NB_A)/NPCOL )*NB_A
π ARGUMENTS π
TRANS(global input)CHARACTERβ= 'N': the linear system involvessub( A );= 'T': the linear system involvessub( A )**T.
M(global input)INTEGERβ The number of rows to be operated on, i.e. the number of rows of the distributed submatrixsub( A ).M >= 0.N(global input)INTEGERβ The number of columns to be operated on, i.e. the number of columns of the distributed submatrixsub( A ).N >= 0.NRHS(global input)INTEGERβ The number of right hand sides, i.e. the number of columns of the distributed submatricessub( B )andX.NRHS >= 0.A(local input/local output)DOUBLE PRECISIONpointer into the local memory to an array of local dimension( LLD_A, LOCc(JA+N-1) ). On entry, the M-by-N matrix A. ifM >= N,sub( A )is overwritten by details of its QR factorization as returned byPDGEQRF; ifM < N,sub( A )is overwritten by details of its LQ factorization as returned byPDGELQF.IA(global input)INTEGERβ The row index in the global arrayAindicating the first row ofsub( A ).JA(global input)INTEGERβ The column index in the global arrayAindicating the first column ofsub( A ).DESCA(global and local input)INTEGERarray of dimensionDLEN_. The array descriptor for the distributed matrixA.B(local input/local output)DOUBLE PRECISIONpointer into the local memory to an array of local dimension(LLD_B, LOCc(JB+NRHS-1)). On entry, this array contains the local pieces of the distributed matrix B of right hand side vectors, stored columnwise;sub( B )is M-by-NRHS ifTRANS='N', and N-by-NRHS otherwise. On exit,sub( B )is overwritten by the solution vectors, stored columnwise: ifTRANS = 'N'andM >= N, rows 1 to N ofsub( B )contain the least squares solution vectors; the residual sum of squares for the solution in each column is given by the sum of squares of elements N+1 to M in that column; ifTRANS = 'N'andM < N, rows 1 to N ofsub( B )contain the minimum norm solution vectors; ifTRANS = 'T'andM >= N, rows 1 to M ofsub( B )contain the minimum norm solution vectors; ifTRANS = 'T'andM < N, rows 1 to M ofsub( B )contain the least squares solution vectors; the residual sum of squares for the solution in each column is given by the sum of squares of elements M+1 to N in that column.IB(global input)INTEGERβ The row index in the global arrayBindicating the first row ofsub( B ).JB(global input)INTEGERβ The column index in the global arrayBindicating the first column ofsub( B ).DESCB(global and local input)INTEGERarray of dimensionDLEN_. The array descriptor for the distributed matrixB.WORK(local workspace/local output)DOUBLE PRECISIONarray, dimension(LWORK). On exit,WORK(1)returns the minimal and optimalLWORK.LWORK(local or global input)INTEGERβ The dimension of the arrayWORK.LWORKis local input and must be at least
LWORK >= LTAU + MAX( LWF, LWS )
where
If M >= N, then
LTAU = NUMROC( JA+MIN(M,N)-1, NB_A, MYCOL, CSRC_A, NPCOL ),
LWF = NB_A * ( MpA0 + NqA0 + NB_A )
LWS = MAX( (NB_A*(NB_A-1))/2, (NRHSqB0 + MpB0)*NB_A ) + NB_A * NB_A
Else
LTAU = NUMROC( IA+MIN(M,N)-1, MB_A, MYROW, RSRC_A, NPROW ),
LWF = MB_A * ( MpA0 + NqA0 + MB_A )
LWS = MAX( (MB_A*(MB_A-1))/2, ( NpB0 + MAX( NqA0 + NUMROC( NUMROC( N+IROFFB, MB_A, 0, 0, NPROW ), MB_A, 0, 0, LCMP ), NRHSqB0 ) )*MB_A ) + MB_A * MB_A
End if
where LCMP = LCM / NPROW with LCM = ILCM( NPROW, NPCOL ),
IROFFA = MOD( IA-1, MB_A ), ICOFFA = MOD( JA-1, NB_A ), IAROW = INDXG2P( IA, MB_A, MYROW, RSRC_A, NPROW ), IACOL = INDXG2P( JA, NB_A, MYCOL, CSRC_A, NPCOL ), MpA0 = NUMROC( M+IROFFA, MB_A, MYROW, IAROW, NPROW ), NqA0 = NUMROC( N+ICOFFA, NB_A, MYCOL, IACOL, NPCOL ),
IROFFB = MOD( IB-1, MB_B ), ICOFFB = MOD( JB-1, NB_B ), IBROW = INDXG2P( IB, MB_B, MYROW, RSRC_B, NPROW ), IBCOL = INDXG2P( JB, NB_B, MYCOL, CSRC_B, NPCOL ), MpB0 = NUMROC( M+IROFFB, MB_B, MYROW, IBROW, NPROW ), NpB0 = NUMROC( N+IROFFB, MB_B, MYROW, IBROW, NPROW ), NRHSqB0 = NUMROC( NRHS+ICOFFB, NB_B, MYCOL, IBCOL, NPCOL ),
ILCM, INDXG2P and NUMROC are ScaLAPACK tool functions; MYROW, MYCOL, NPROW and NPCOL can be determined by calling the subroutine BLACS_GRIDINFO.
If LWORK = -1, then LWORK is global input and a workspace query is assumed; the routine only calculates the minimum and optimal size for all work arrays. Each of these values is returned in the first entry of the corresponding work array, and no error message is issued by PXERBLA.
INFO(global output)INTEGERβ= 0: successful exit< 0: If the i-th argument is an array and the j-entry had an illegal value, thenINFO = -(i*100+j), if the i-th argument is a scalar and had an illegal value, thenINFO = -i.
π thatβs the whole vibe: LAPACK version 1.5, dated 12 May 1997, and this page is PDGELS(l) β bye bestie, go slay some linear systems! π
π¦ where this page comes from
- ubuntu jammy: scalapack-doc 1.5-11
- π girlified by deepseek-ai/DeepSeek-V4.1-Flash on : a language model's rewrite of the original, under its terms