SRC/clarrv.f(3)
β¨ SRC/clarrv.f(3) β Library Functions Manual β¨
πΈ NAME πΈ
SRC/clarrv.f β bestie, this is the page for the clarrv routine, and the synopsis line is right there: CLARRV computes the eigenvectors of the tridiagonal matrix T = L D LT given L, D and the eigenvalues of L D LT.
π¦ SYNOPSIS π¦
π«Ά Functions/Subroutines π«Ά
subroutine clarrv (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)
CLARRV computes the eigenvectors of the tridiagonal matrix T = L D LT given L, D and the eigenvalues of L D LT. Slay.
π₯ Function/Subroutine Documentation π₯
subroutine clarrv (integer n, real vl, real vu, real, dimension( * ) d,
real, dimension( * ) l, real pivmin, integer, dimension( * ) isplit,
integer m, integer dol, integer dou, real minrgp, real rtol1,
real rtol2, real, dimension( * ) w, real, dimension( * ) werr, real,
dimension( * ) wgap, integer, dimension( * ) iblock, integer, dimension(
* ) indexw, real, dimension( * ) gers, complex, dimension( ldz, * ) z,
integer ldz, integer, dimension( * ) isuppz, real, dimension( * ) work,
integer, dimension( * ) iwork, integer info)
CLARRV computes the eigenvectors of the tridiagonal matrix T = L D LT given L, D and the eigenvalues of L D LT. Iconic behavior, honestly.
π§Έ Purpose π§Έ
Okay so the actual purpose, straight from the source β and yes, the source wraps these lines in Doxygen !> markers, iykyk:
CLARRV 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 SLARRE.
π Parameters π
NβNisINTEGER. The order of the matrix.N >= 0.VLβVLisREAL. 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 desired RANGE.VUβVUisREAL. Upper 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 desired RANGE.DβDisREAL array, dimension (N). On entry, theNdiagonal elements of the diagonal matrixD. On exit,Dmay be overwritten.LβLisREAL array, dimension (N). On entry, the(N-1)subdiagonal elements of the unit bidiagonal matrixLare in elements 1 toN-1ofL(if the matrix is not split.) At the end of each block is stored the corresponding shift as given bySLARRE. On exit,Lis overwritten.PIVMINβPIVMINisREAL. 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/columns 1 toISPLIT( 1 ), the second of rows/columnsISPLIT( 1 )+1throughISPLIT( 2 ), etc.MβMisINTEGER. The total number of input eigenvalues.0 <= M <= N.DOLβDOLisINTEGER. That's all the source says for this one, bestie.DOUβDOUisINTEGER. If the user wants to compute only selected eigenvectors from all the eigenvalues supplied, he 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.MINRGPβMINRGPisREAL. No extra description in the source for this one.RTOL1βRTOL1isREAL. No extra description in the source for this one.RTOL2βRTOL2isREAL. Parameters for bisection. An interval[LEFT,RIGHT]has converged ifRIGHT-LEFT < MAX( RTOL1*GAP, RTOL2*MAX(|LEFT|,|RIGHT|) ).WβWisREAL 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 arrayWfromSLARREis 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.WERRβWERRisREAL array, dimension (N). The firstMelements contain the semiwidth of the uncertainty interval of the corresponding eigenvalue inW.WGAPβWGAPisREAL 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 the 10-th eigenvalue in the second block.GERSβGERSisREAL 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.ZβZisCOMPLEX 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 ).WORKβWORKisREAL array, dimension (12*N).IWORKβIWORKisINTEGER array, dimension (7*N).INFOβINFOisINTEGER.= 0: successful exit> 0: A problem occurred in CLARRV.< 0: One of the called subroutines signaled an internal problem. Needs inspection of the corresponding parameterIINFOfor further information.=-1: Problem inSLARRBwhen refining a child's eigenvalues.=-2: Problem inSLARRFwhen 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 inSLARRBwhen 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 TennesseeUniv. of California BerkeleyUniv. of Colorado DenverNAG 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 281 of file clarrv.f.
π§ Author π§
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