SRC/dstemr.f(3)

Hey bestie 💖 — welcome to SRC/dstemr.f(3) from the Library Functions Manual. This is the DSTEMR page, and it's lowkey iconic: real symmetric tridiagonal matrix energy, eigenvalues, eigenvectors, and every single parameter keeping its exact deets. No cap. 🦋

📛 NAME

SRC/dstemr.f — that's the page/file name, and the star of the show is DSTEMR. 👑

🛠️ SYNOPSIS

🧩 Functions/Subroutines

subroutine dstemr (jobz, range, n, d, e, vl, vu, il, iu, m, w, z, ldz,
     nzc, isuppz, tryrac, work, lwork, iwork, liwork, info)

DSTEMR

✨ Function/Subroutine Documentation ✨

🧬 subroutine dstemr

subroutine dstemr (character jobz, character range, integer n, double
     precision, dimension( * ) d, double precision, dimension( * ) e, double
     precision vl, double precision vu, integer il, integer iu, integer m,
     double precision, dimension( * ) w, double precision, dimension( ldz, *
     ) z, integer ldz, integer nzc, integer, dimension( * ) isuppz, logical
     tryrac, double precision, dimension( * ) work, integer lwork, integer,
     dimension( * ) iwork, integer liwork, integer info)
     DSTEMR

DSTEMR

🎯 Purpose

okay so DSTEMR computes selected eigenvalues and, optionally, eigenvectors of a real symmetric tridiagonal matrix T. Any such unreduced matrix has a well defined set of pairwise different real eigenvalues, and the corresponding real eigenvectors are pairwise orthogonal. periodt. ✨

The spectrum may be computed either completely or partially by specifying either an interval (VL,VU] or a range of indices IL:IU for the desired eigenvalues. You get to choose the vibe: full spectrum or selected faves. 💅

Depending on the number of desired eigenvalues, these are computed either by bisection or the dqds algorithm. Numerically orthogonal eigenvectors are computed by the use of various suitable L D L^T factorizations near clusters of close eigenvalues (referred to as RRRs, Relatively Robust Representations). An informal sketch of the algorithm follows.

For each unreduced block (submatrix) of T, here's the tea:

1. (a) Compute T - sigma I = L D L^T, so that L and D define all the wanted eigenvalues to high relative accuracy. This means that small relative changes in the entries of D and L cause only small relative changes in the eigenvalues and eigenvectors. The standard (unfactored) representation of the tridiagonal matrix T does not have this property in general. 😭
2. (b) Compute the eigenvalues to suitable accuracy. If the eigenvectors are desired, the algorithm attains full accuracy of the computed eigenvalues only right before the corresponding vectors have to be computed, see steps c) and d).
3. (c) For each cluster of close eigenvalues, select a new shift close to the cluster, find a new factorization, and refine the shifted eigenvalues to suitable accuracy. Clusters getting their own little glow-up. 🎀
4. (d) For each eigenvalue with a large enough relative separation compute the corresponding eigenvector by forming a rank revealing twisted factorization. Go back to (c) for any clusters that remain. Slay. 🔥

For more details, see:

• Inderjit S. Dhillon and Beresford N. Parlett: Linear Algebra and its Applications, 387(1), pp. 1-28, August 2004.
• Inderjit Dhillon and Beresford Parlett: SIAM Journal on Matrix Analysis and Applications, Vol. 25, 2004. Also LAPACK Working Note 154.
• Inderjit Dhillon: , Computer Science Division Technical Report No. UCB/CSD-97-971, UC Berkeley, May 1997.

🌈 Further Details

⚠️ Further Details: 1.DSTEMR works only on machines which follow IEEE-754 floating-point standard in their handling of infinities and NaNs. This permits the use of efficient inner loops avoiding a check for zero divisors. iykyk — that IEEE-754 dependence is real, bestie.

🎛️ Parameters

🎀 JOBZ

JOBZ is CHARACTER*1.

• = 'N' — compute eigenvalues only;
• = 'V' — compute eigenvalues and eigenvectors.

🎀 RANGE

RANGE is CHARACTER*1.

• = 'A' — all eigenvalues will be found.
• = 'V' — all eigenvalues in the half-open interval (VL,VU] will be found.
• = 'I' — the IL-th through IU-th eigenvalues will be found.

🎀 N

N is INTEGER. The order of the matrix. N >= 0.

🎀 D

D is DOUBLE PRECISION array, dimension (N). On entry, the N diagonal elements of the tridiagonal matrix T. On exit, D is overwritten.

🎀 E

E is DOUBLE PRECISION array, dimension (N). On entry, the (N-1) subdiagonal elements of the tridiagonal matrix T in elements 1 to N-1 of E. E(N) need not be set on input, but is used internally as workspace. On exit, E is overwritten.

🎀 VL

VL is DOUBLE PRECISION.

If RANGE='V', the lower bound of the interval to be searched for eigenvalues. VL < VU. Not referenced if RANGE = 'A' or 'I'.

🎀 VU

VU is DOUBLE PRECISION.

If RANGE='V', the upper bound of the interval to be searched for eigenvalues. VL < VU. Not referenced if RANGE = 'A' or 'I'.

🎀 IL

IL is INTEGER.

If RANGE='I', the index of the smallest eigenvalue to be returned. 1 <= IL <= IU <= N, if N > 0. Not referenced if RANGE = 'A' or 'V'.

🎀 IU

IU is INTEGER.

If RANGE='I', the index of the largest eigenvalue to be returned. 1 <= IL <= IU <= N, if N > 0. Not referenced if RANGE = 'A' or 'V'.

🎀 M

M is INTEGER. The total number of eigenvalues found. 0 <= M <= N. If RANGE = 'A', M = N, and if RANGE = 'I', M = IU-IL+1.

🎀 W

W is DOUBLE PRECISION array, dimension (N). The first M elements contain the selected eigenvalues in ascending order. ascending order, naturally — we're not chaotic. 💅

🎀 Z

Z is DOUBLE PRECISION array, dimension (LDZ, max(1,M) ).

If JOBZ = 'V', and if INFO = 0, then the first M columns of Z contain the orthonormal eigenvectors of the matrix T corresponding to the selected eigenvalues, with the i-th column of Z holding the eigenvector associated with W(i). If JOBZ = 'N', then Z is not referenced.

Note: the user must ensure that at least max(1,M) columns are supplied in the array Z; if RANGE = 'V', the exact value of M is not known in advance and can be computed with a workspace query by setting NZC = -1, see below.

🎀 LDZ

LDZ is INTEGER. The leading dimension of the array Z. LDZ >= 1, and if JOBZ = 'V', then LDZ >= max(1,N).

🎀 NZC

NZC is INTEGER. The number of eigenvectors to be held in the array Z.

If RANGE = 'A', then NZC >= max(1,N). If RANGE = 'V', then NZC >= the number of eigenvalues in (VL,VU]. If RANGE = 'I', then NZC >= IU-IL+1.

If NZC = -1, then a workspace query is assumed; the routine calculates the number of columns of the array Z that are needed to hold the eigenvectors. This value is returned as the first entry of the Z array, and no error message related to NZC is issued by XERBLA.

🎀 ISUPPZ

ISUPPZ is INTEGER array, dimension ( 2*max(1,M) ). The support of the eigenvectors in Z, i.e., the indices indicating the nonzero elements in Z. The i-th computed eigenvector is nonzero only in elements ISUPPZ( 2*i-1 ) through ISUPPZ( 2*i ). This is relevant in the case when the matrix is split. ISUPPZ is only accessed when JOBZ is 'V' and N > 0. vibe check: support indices only when they matter.

🎀 TRYRAC

TRYRAC is LOGICAL.

If TRYRAC = .TRUE., indicates that the code should check whether the tridiagonal matrix defines its eigenvalues to high relative accuracy. If so, the code uses relative-accuracy preserving algorithms that might be (a bit) slower depending on the matrix. If the matrix does not define its eigenvalues to high relative accuracy, the code can use possibly faster algorithms.

If TRYRAC = .FALSE., the code is not required to guarantee relatively accurate eigenvalues and can use the fastest possible techniques.

On exit, a .TRUE. TRYRAC will be set to .FALSE. if the matrix does not define its eigenvalues to high relative accuracy.

🎀 WORK

WORK is DOUBLE PRECISION array, dimension (LWORK). On exit, if INFO = 0, WORK(1) returns the optimal (and minimal) LWORK.

🎀 LWORK

LWORK is INTEGER. The dimension of the array WORK. LWORK >= max(1,18*N) if JOBZ = 'V', and LWORK >= max(1,12*N) if JOBZ = '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.

🎀 IWORK

IWORK is INTEGER array, dimension (LIWORK). On exit, if INFO = 0, IWORK(1) returns the optimal LIWORK.

🎀 LIWORK

LIWORK is INTEGER. The dimension of the array IWORK. LIWORK >= max(1,10*N) if the eigenvectors are desired, and LIWORK >= max(1,8*N) if only the eigenvalues are to be computed.

If LIWORK = -1, then a workspace query is assumed; the routine only calculates the optimal size of the IWORK array, returns this value as the first entry of the IWORK array, and no error message related to LIWORK is issued by XERBLA.

💖 INFO

(the INFO deets are literally up next, bestie — keep scrolling ✨)

• INFO — is INTEGER. On exit, INFO tells you what went down:
  • = 0: successful exit, slay! 🎉
  • < 0: if INFO = -i, the i-th argument had an illegal value. Not cute. 🚩
  • > 0: if INFO = 1X, internal error in DLARRE; if INFO = 2X, internal error in DLARRV. Here, the digit X = ABS( IINFO ) < 10, where IINFO is the nonzero error code returned by DLARRE or DLARRV, respectively. Yeah, that's a whole thing. 💀

Author

• Univ. of Tennessee
• Univ. of California Berkeley
• Univ. of Colorado Denver
• NAG Ltd.

These are the academic and corporate icons behind it. 👑

Contributors

• 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
• Aravindh Krishnamoorthy, FAU, Erlangen, Germany

The way they all contributed… we love to see it. 🫶

📍 Definition at line 319 of file dstemr.f. (This is where the magic lives in the source.)

Author

🤖 This page was generated automatically by Doxygen for LAPACK from the source code. No cap, it's a robot thing.

---

And that's the whole vibe check on LAPACK Version 3.12.0 — page SRC/dstemr.f(3). Stay slay, bestie! 💅✨