SRC/dlarrv.f(3)
SRC/dlarrv.f(3) β Library Functions Manual
Okay bestie, welcome to the dlarrv page. Itβs giving Fortran, itβs giving eigenvectors, and itβs about to make your linear algebra era iconic. π
β¨ NAME β¨
SRC/dlarrv.f
π¦ SYNOPSIS π¦
π§ Functions/Subroutines π§
subroutine dlarrv (n, vl, vu, d, l, pivmin, isplit, m, dol, dou, minrgp,
rtol1, rtol2, w, werr, wgap, iblock, indexw, gers, z, ldz, isuppz,
work, iwork, info)
DLARRV computes the eigenvectors of the tridiagonal matrix T = L D LT given L, D and the eigenvalues of L D LT.
π Function/Subroutine Documentation π
π«Ά subroutine dlarrv π«Ά
subroutine dlarrv (integer n, double precision vl, double precision vu,
double precision, dimension( * ) d, double precision, dimension( * ) l,
double precision pivmin, integer, dimension( * ) isplit, integer m,
integer dol, integer dou, double precision minrgp, double precision
rtol1, double precision rtol2, double precision, dimension( * ) w,
double precision, dimension( * ) werr, double precision, dimension( * )
wgap, integer, dimension( * ) iblock, integer, dimension( * ) indexw,
double precision, dimension( * ) gers, double precision, dimension( ldz,
* ) z, integer ldz, integer, dimension( * ) isuppz, double precision,
dimension( * ) work, integer, dimension( * ) iwork, integer info)
DLARRV computes the eigenvectors of the tridiagonal matrix T = L D LT given L, D and the eigenvalues of L D LT.
π Purpose: π
Okay so hereβs the vibe: DLARRV computes the eigenvectors of the tridiagonal matrix T = L D L**T given L, D and APPROXIMATIONS to the eigenvalues of L D L**T. The input eigenvalues should have been computed by DLARRE.
π Parameters π
NβNisINTEGER. The order of the matrix.N >= 0.VLβVLisDOUBLE PRECISION. Lower bound of the interval that contains the desired eigenvalues.VL < VU. Needed to compute gaps on the left or right end of the extremal eigenvalues in the desiredRANGE.VUβVUisDOUBLE PRECISION. Upper bound of the interval that contains the desired eigenvalues.VL < VU. Note:VUis currently not used by this implementation ofDLARRV;VUis passed toDLARRVbecause it could be used compute gaps on the right end of the extremal eigenvalues. However, with not much initial accuracy inLAMBDAandVU, the formula can lead to an overestimation of the right gap and thus to inadequately early RQI 'convergence'. This is currently prevented by forcing a small right gap. And so it turns out thatVUis currently not used by this implementation ofDLARRV.DβDisDOUBLE PRECISION array, dimension(N). On entry, theNdiagonal elements of the diagonal matrixD. On exit,Dmay be overwritten.LβLisDOUBLE PRECISION array, dimension(N). On entry, the(N-1)subdiagonal elements of the unit bidiagonal matrixLare in elements1toN-1ofL(if the matrix is not split.) At the end of each block is stored the corresponding shift as given byDLARRE. On exit,Lis overwritten.PIVMINβPIVMINisDOUBLE PRECISION. The minimum pivot allowed in the Sturm sequence.ISPLITβISPLITisINTEGER array, dimension(N). The splitting points, at whichTbreaks up into blocks. The first block consists of rows/columns1toISPLIT( 1 ), the second of rows/columnsISPLIT( 1 )+1throughISPLIT( 2 ), etc.MβMisINTEGER. The total number of input eigenvalues.0 <= M <= N.DOLβDOLisINTEGER.DOUβDOUisINTEGER. If the user wants to compute only selected eigenvectors from all the eigenvalues supplied, they can specify an index rangeDOL:DOU. Or else the settingDOL=1,DOU=Mshould be applied. Note thatDOLandDOUrefer to the order in which the eigenvalues are stored inW. If the user wants to compute only selected eigenpairs, then the columnsDOL-1toDOU+1of the eigenvector spaceZcontain the computed eigenvectors. All other columns ofZare set to zero.MINRGPβMINRGPisDOUBLE PRECISION.RTOL1βRTOL1isDOUBLE PRECISION.RTOL2βRTOL2isDOUBLE PRECISION. Parameters for bisection. An interval[LEFT,RIGHT]has converged ifRIGHT-LEFT < MAX( RTOL1*GAP, RTOL2*MAX(|LEFT|,|RIGHT|) ).WβWisDOUBLE PRECISION array, dimension(N). The firstMelements ofWcontain the APPROXIMATE eigenvalues for which eigenvectors are to be computed. The eigenvalues should be grouped by split-off block and ordered from smallest to largest within the block (The output arrayWfromDLARREis expected here). Furthermore, they are with respect to the shift of the corresponding root representation for their block. On exit,Wholds the eigenvalues of the UNshifted matrix.WERRβWERRisDOUBLE PRECISION array, dimension(N). The firstMelements contain the semiwidth of the uncertainty interval of the corresponding eigenvalue inW.WGAPβWGAPisDOUBLE PRECISION array, dimension(N). The separation from the right neighbor eigenvalue inW.IBLOCKβIBLOCKisINTEGER array, dimension(N). The indices of the blocks (submatrices) associated with the corresponding eigenvalues inW;IBLOCK(i)=1if eigenvalueW(i)belongs to the first block from the top,=2ifW(i)belongs to the second block, etc.INDEXWβINDEXWisINTEGER array, dimension(N). The indices of the eigenvalues within each block (submatrix); for example,INDEXW(i)= 10andIBLOCK(i)=2imply that thei-th eigenvalueW(i)is the10-th eigenvalue in the second block.GERSβGERSisDOUBLE PRECISION array, dimension(2*N). TheNGerschgorin intervals (thei-th Gerschgorin interval is(GERS(2*i-1), GERS(2*i)). The Gerschgorin intervals should be computed from the original UNshifted matrix.ZβZisDOUBLE PRECISION array, dimension(LDZ, max(1,M) ). IfINFO = 0, the firstMcolumns ofZcontain the orthonormal eigenvectors of the matrixTcorresponding to the input eigenvalues, with thei-th column ofZholding the eigenvector associated withW(i). Note: the user must ensure that at leastmax(1,M)columns are supplied in the arrayZ.LDZβLDZisINTEGER. The leading dimension of the arrayZ.LDZ >= 1, and ifJOBZ = 'V',LDZ >= max(1,N).ISUPPZβISUPPZisINTEGER array, dimension( 2*max(1,M) ). The support of the eigenvectors inZ, i.e., the indices indicating the nonzero elements inZ. TheI-th eigenvector is nonzero only in elementsISUPPZ( 2*I-1 )throughISUPPZ( 2*I ).WORKβWORKisDOUBLE PRECISION array, dimension(12*N).IWORKβIWORKisINTEGER array, dimension(7*N).INFOβINFOisINTEGER.= 0: successful exit> 0: A problem occurred inDLARRV.< 0: One of the called subroutines signaled an internal problem. Needs inspection of the corresponding parameterIINFOfor further information.=-1: Problem inDLARRBwhen refining a child's eigenvalues.=-2: Problem inDLARRFwhen computing the RRR of a child. When a child is inside a tight cluster, it can be difficult to find an RRR. A partial remedy from the user's point of view is to make the parameterMINRGPsmaller and recompile. However, as the orthogonality of the computed vectors is proportional to1/MINRGP, the user should be aware that he might be trading in precision when he decreasesMINRGP.=-3: Problem inDLARRBwhen refining a single eigenvalue after the Rayleigh correction was rejected.= 5: The Rayleigh Quotient Iteration failed to converge to full accuracy inMAXITRsteps.
π Author π
- Univ. of Tennessee
- Univ. of California Berkeley
- Univ. of Colorado Denver
- NAG Ltd.
π Contributors: π
- Beresford Parlett, University of California, Berkeley, USA
- Jim Demmel, University of California, Berkeley, USA
- Inderjit Dhillon, University of Texas, Austin, USA
- Osni Marques, LBNL/NERSC, USA
- Christof Voemel, University of California, Berkeley, USA
Definition at line 287 of file dlarrv.f.
π Author π
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