PDGECON(l)
๐ PDGECON โ the LAPACK routine, bestie edition ๐
okay so before we even get into it: this is PDGECON from LAPACK, version 1.5. Yes, the (l) means it's a manual page, we're being fancy today. Sit down, grab a strawberry drink ๐, and let's get into it.
โจ NAME โจ
PDGECON โ she estimates the reciprocal of the condition number of a general distributed real matrix A(IA:IA+N-1,JA:JA+N-1), in either the 1-norm or the infinity-norm, using the LU factorization computed by PDGETRF. queen behavior, honestly ๐
๐ฆ SYNOPSIS ๐ฆ
SUBROUTINE PDGECON( NORM, N, A, IA, JA, DESCA, ANORM, RCOND, WORK,
LWORK, IWORK, LIWORK, INFO )
CHARACTER NORM
INTEGER IA, INFO, JA, LIWORK, LWORK, N
DOUBLE PRECISION ANORM, RCOND
INTEGER DESCA( * ), IWORK( * )
DOUBLE PRECISION A( * ), WORK( * )
look at her. iconic signature. no notes. ๐
๐ธ PURPOSE ๐ธ
PDGECON estimates the reciprocal of the condition number of a general distributed real matrix A(IA:IA+N-1,JA:JA+N-1), in either the 1-norm or the infinity-norm, using the LU factorization computed by PDGETRF. literally what the NAME said, but with โจcommitmentโจ.
An estimate is obtained for norm(inv(A(IA:IA+N-1,JA:JA+N-1))), and the reciprocal of the condition number is computed as
RCOND = 1 / ( norm( A(IA:IA+N-1,JA:JA+N-1) ) *
norm( inv(A(IA:IA+N-1,JA:JA+N-1)) ) ).
that's the whole formula. she's giving math and she's serving it. ๐งฎ๐
๐ Notes ๐
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. (iykyk โ it's the little passport that says which process owns which number.)
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".
DTYPE_A(global) โ stored inDESCA( DTYPE_ )โ the descriptor type. In this case,DTYPE_A = 1.CTXT_A(global) โ stored inDESCA( CTXT_ )โ 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) โ stored inDESCA( M_ )โ the number of rows in the global array A.N_A(global) โ stored inDESCA( N_ )โ the number of columns in the global array A.MB_A(global) โ stored inDESCA( MB_ )โ the blocking factor used to distribute the rows of the array.NB_A(global) โ stored inDESCA( NB_ )โ the blocking factor used to distribute the columns of the array.RSRC_A(global) โ stored inDESCA( RSRC_ )โ the process row over which the first row of the array A is distributed.CSRC_A(global) โ stored inDESCA( CSRC_ )โ the process column over which the first column of the array A is distributed.LLD_A(local) โ stored inDESCA( LLD_ )โ 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
๐ ARGUMENTS ๐
NORMโ (global input)CHARACTER. Specifies whether the 1-norm condition number or the infinity-norm condition number is required:= '1'or'O': 1-norm= 'I': Infinity-norm
Nโ (global input)INTEGER. The order of the distributed matrixA(IA:IA+N-1,JA:JA+N-1).N >= 0.Aโ (local input)DOUBLE PRECISIONpointer into the local memory to an array of dimension( LLD_A, LOCc(JA+N-1) ). On entry, this array contains the local pieces of the factorsLandUfrom the factorizationA(IA:IA+N-1,JA:JA+N-1) = P*L*U; the unit diagonal elements ofLare not stored.IAโ (global input)INTEGER. The row index in the global arrayAindicating the first row ofsub( A ).JAโ (global input)INTEGER. The column index in the global arrayAindicating the first column ofsub( A ).DESCAโ (global and local input)INTEGERarray of dimensionDLEN_. The array descriptor for the distributed matrixA.ANORMโ (global input)DOUBLE PRECISION. IfNORM = '1'or'O', the 1-norm of the original distributed matrixA(IA:IA+N-1,JA:JA+N-1). IfNORM = 'I', the infinity-norm of the original distributed matrixA(IA:IA+N-1,JA:JA+N-1).RCONDโ (global output)DOUBLE PRECISION. The reciprocal of the condition number of the distributed matrixA(IA:IA+N-1,JA:JA+N-1), computed as
RCOND = 1 / ( norm( A(IA:IA+N-1,JA:JA+N-1) ) *
norm( inv(A(IA:IA+N-1,JA:JA+N-1)) ) ).
WORKโ (local workspace/local output)DOUBLE PRECISIONarray, dimension(LWORK). On exit,WORK(1)returns the minimal and optimalLWORK.LWORKโ (local or global input)INTEGER. The dimension of the arrayWORK.LWORKis local input and must be at least
LWORK >= 2*LOCr(N+MOD(IA-1,MB_A)) +
2*LOCc(N+MOD(JA-1,NB_A)) + MAX( 2, MAX( NB_A*MAX( 1,
CEIL(NPROW-1,NPCOL) ), LOCc(N+MOD(JA-1,NB_A)) + NB_A*MAX( 1,
CEIL(NPCOL-1,NPROW) ) ).
LOCr and LOCc values can be computed using the ScaLAPACK tool function NUMROC; 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.
IWORKโ (local workspace/local output)INTEGERarray, dimension(LIWORK). On exit,IWORK(1)returns the minimal and optimalLIWORK.LIWORKโ (local or global input)INTEGER. The dimension of the arrayIWORK.LIWORKis local input and must be at leastLIWORK >= LOCr(N+MOD(IA-1,MB_A)).
If LIWORK = -1, then LIWORK 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.
INFOโ (global output)INTEGER.= 0: successful exit ๐< 0: If thei-th argument is an array and thej-entry had an illegal value, thenINFO = -(i*100+j), if thei-th argument is a scalar and had an illegal value, thenINFO = -i.
not me reading INFO = -(i*100+j) and having to sit down. it's giving encrypted message from the compiler. it's giving you WILL decode it. ๐ซถ
that's the whole page, bestie โ nothing skipped, nothing summarised, every flag and formula accounted for. ๐
this was LAPACK version 1.5, dated 12 May 1997 โ yes she's a vintage icon and she still slays. ๐
kisses, take care of your condition numbers, and remember: RCOND close to zero means your matrix is out here struggling. bye bestie! ๐
โจ๐
๐ฆ where this page comes from
- ubuntu jammy: scalapack-doc 1.5-11
- ๐ girlified by deepseek-ai/DeepSeek-V4.1-Flash on : a language model's rewrite of the original, under its terms