CGEJSV(3)

📦 version 3.12.0, in opensuse-leap 16.0

🎀 NAME 🎀

Ok bestie, welcome to CGEJSV(3) — yes, the Library Functions Manual one 💅. The header path snippet is /home/abui...RC/cgejsv.f(3), and the NAME line points at /home/abuild/rpmbuild/BUILD/lapack-static-3.12.0-build/lapack-3.12.0/SRC/cgejsv.f 🫶.

✨ SYNOPSIS ✨

🌈 Functions/Subroutines 🌈

       subroutine CGEJSV (joba, jobu, jobv, jobr, jobt, jobp, m, n, a, lda,
           sva, u, ldu, v, ldv, cwork, lwork, rwork, lrwork, iwork, info)
           CGEJSV

It’s CGEJSV, living its best subroutine life 💖.

💖 Function/Subroutine Documentation 💖

   subroutine CGEJSV (character*1 joba, character*1 jobu, character*1 jobv,
       character*1 jobr, character*1 jobt, character*1 jobp, integer m, integer
       n, complex, dimension( lda, * ) a, integer lda, real, dimension( n )
       sva, complex, dimension( ldu, * ) u, integer ldu, complex, dimension(
       ldv, * ) v, integer ldv, complex, dimension( lwork ) cwork, integer
       lwork, real, dimension( lrwork ) rwork, integer lrwork, integer,
       dimension( * ) iwork, integer info)

CGEJSV again — the source is extra like that, and honestly? Iconic 💁‍♀️.

🌸 Purpose 🌸

Bestie, here’s the whole vibe: CGEJSV computes the singular value decomposition (SVD) of a complex M-by-N matrix [A], where M >= N. The SVD of [A] is written as

[A] = [U] * [SIGMA] * [V]^*,

where [SIGMA] is an N-by-N (M-by-N) matrix which is zero except for its N diagonal elements, [U] is an M-by-N (or M-by-M) unitary matrix, and [V] is an N-by-N unitary matrix. The diagonal elements of [SIGMA] are the singular values of [A]. The columns of [U] and [V] are the left and the right singular vectors of [A], respectively. The matrices [U] and [V] are computed and stored in the arrays U and V, respectively. The diagonal of [SIGMA] is computed and stored in the array SVA ✨.

🎀 Parameters 🎀

JOBA

JOBA is CHARACTER*1 — it specifies the level of accuracy 🎚️.

The input matrix is preprocessed with the QRF with column pivoting. This initial preprocessing and preconditioning by a rank revealing QR factorization is common for all values of JOBA. Additional actions are specified as follows:

  • 'C' — This option works well (high relative accuracy) if A = B * D, with well-conditioned B and arbitrary diagonal matrix D. The accuracy cannot be spoiled by COLUMN scaling. The accuracy of the computed output depends on the condition of B, and the procedure aims at the best theoretical accuracy. The relative error max_{i=1:N}|d sigma_i| / sigma_i is bounded by f(M,N)*epsilon* cond(B), independent of D 💅.
  • 'E' — Computation as with 'C' with an additional estimate of the condition number of B. It provides a realistic error bound 👑.
  • 'F' — If A = D1 * C * D2 with ill-conditioned diagonal scalings D1, D2, and well-conditioned matrix C, this option gives higher accuracy than the 'C' option. If the structure of the input matrix is not known, and relative accuracy is desirable, then this option is advisable. The input matrix A is preprocessed with QR factorization with FULL (row and column) pivoting 🌈.
  • 'G' — Computation as with 'F' with an additional estimate of the condition number of B, where A=B*D. If A has heavily weighted rows, then using this condition number gives too pessimistic error bound 😭.
  • 'A' — Small singular values are not well determined by the data and are considered as noisy; the matrix is treated as numerically rank deficient. The error in the computed singular values is bounded by f(m,n)*epsilon*||A||. The computed SVD A = U * S * V^* restores A up to f(m,n)*epsilon*||A||. This gives the procedure the licence to discard (set to zero) all singular values below N*epsilon*||A|| 💀.
  • 'R' — Similar as in 'A'. Rank revealing property of the initial QR factorization is used to reveal (using triangular factor) a gap sigma_{r+1} < epsilon * sigma_r in which case the numerical RANK is declared to be r. The SVD is computed with absolute error bounds, but more accurately than with 'A' 🔥.

JOBU

JOBU is CHARACTER*1 — it specifies whether to compute the columns of U 🦋.

  • 'U' — N columns of U are returned in the array U.
  • 'F' — full set of M left sing. vectors is returned in the array U.
  • 'W' — U may be used as workspace of length M*N. See the description of U.
  • 'N' — U is not computed.

JOBV

JOBV is CHARACTER*1 — it specifies whether to compute the matrix V 🎀.

  • 'V' — N columns of V are returned in the array V; Jacobi rotations are not explicitly accumulated.
  • 'J' — N columns of V are returned in the array V, but they are computed as the product of Jacobi rotations, if JOBT = 'N'.
  • 'W' — V may be used as workspace of length N*N. See the description of V.
  • 'N' — V is not computed.

JOBR

JOBR is CHARACTER*1 — it specifies the RANGE for the singular values. Issues the licence to set to zero small positive singular values if they are outside specified range 🚨.

If A .NE. 0 is scaled so that the largest singular value of c*A is around SQRT(BIG), BIG=SLAMCH('O'), then JOBR issues the licence to kill columns of A whose norm in c*A is less than SQRT(SFMIN) (for JOBR = 'R'), or less than SMALL=SFMIN/EPSLN, where SFMIN=SLAMCH('S'), EPSLN=SLAMCH('E') 😵‍💫.

  • 'N' — Do not kill small columns of c*A. This option assumes that BLAS and QR factorizations and triangular solvers are implemented to work in that range. If the condition of A is greater than BIG, use CGESVJ 💀.
  • 'R' — RESTRICTED range for sigma(c*A) is [SQRT(SFMIN), SQRT(BIG)] (roughly, as described above). This option is recommended 👑.
=========================== For computing the singular values in the FULL range [SFMIN,BIG] use CGESVJ ✨.

JOBT

JOBT is CHARACTER*1 — if the matrix is square then the procedure may determine to use transposed A if A^* seems to be better with respect to convergence. If the matrix is not square, JOBT is ignored. The decision is based on two values of entropy over the adjoint orbit of A^* * A. See the descriptions of RWORK(6) and RWORK(7) 🧚.

  • 'T' — transpose if entropy test indicates possibly faster convergence of Jacobi process if A^* is taken as input. If A is replaced with A^*, then the row pivoting is included automatically.
  • 'N' — do not speculate.

The option 'T' can be used to compute only the singular values, or the full SVD (U, SIGMA and V). For only one set of singular vectors (U or V), the caller should provide both U and V, as one of the matrices is used as workspace if the matrix A is transposed. The implementer can easily remove this constraint and make the code more complicated. See the descriptions of U and V 🫶.

In general, this option is considered experimental, and 'N'; should be preferred. This is subject to changes in the future 🙈.

JOBP

JOBP is CHARACTER*1 — issues the licence to introduce structured perturbations to drown denormalized numbers. This licence should be active if the denormals are poorly implemented, causing slow computation, especially in cases of fast convergence (!). For details see [1,2] 😭.

For the sake of simplicity, this perturbations are included only when the full SVD or only the singular values are requested. The implementer/user can easily add the perturbation for the cases of computing one set of singular vectors.

  • 'P' — introduce perturbation.
  • 'N' — do not perturb.

M

M is INTEGER — the number of rows of the input matrix A. M >= 0 🌸.

N

N is INTEGER — the number of columns of the input matrix A. M >= N >= 0 🌸.

A

A is COMPLEX array, dimension (LDA,N) — on entry, the M-by-N matrix A 💜.

LDA

LDA is INTEGER — the leading dimension of the array A. LDA >= max(1,M) 💜.

SVA

SVA is REAL array, dimension (N) — on exit:

  • For RWORK(1)/RWORK(2) = ONE: The singular values of A. During the computation SVA contains Euclidean column norms of the iterated matrices in the array A.
  • For RWORK(1) .NE. RWORK(2): The singular values of A are (RWORK(1)/RWORK(2)) * SVA(1:N). This factored form is used if sigma_max(A) overflows or if small singular values have been saved from underflow by scaling the input matrix A.
  • If JOBR='R' then some of the singular values may be returned as exact zeros obtained by [blank in the source, bestie 😭] because they are below the numerical rank threshold or are denormalized numbers.

U

  • U — COMPLEX array, dimension ( LDU, N ) or ( LDU, M )
    • If JOBU = 'U', then on exit U is holding the M-by-N matrix of the left singular vectors 💜
    • If JOBU = 'F', then on exit U is holding the M-by-M matrix of the left singular vectors, including an ONB of the orthogonal complement of Range(A). yes bestie, the whole complement, we're not leaving anyone out ✨
    • If JOBU = 'W' .AND. (JOBV = 'V' .AND. JOBT = 'T' .AND. M = N), then U gets used as workspace if the procedure replaces A with A^. In that case, [V] is computed in U as left singular vectors of A^ and then copied back to the V array. This 'W' option is literally just a reminder to the caller that in this case U is reserved as workspace of length N*N. it's giving "heads up, I'm borrowing your car" 🫶
    • If JOBU = 'N', U is not referenced, unless JOBT='T'.
  • LDU — INTEGER

The leading dimension of the array U, LDU >= 1. And if JOBU = 'U' or 'F' or 'W', then LDU >= M. no skipping this one, it's the vibe check for your array bounds 🎀

  • V — COMPLEX array, dimension ( LDV, N )
    • If JOBV = 'V', 'J' then on exit V holds the N-by-N matrix of the right singular vectors 🌸
    • If JOBV = 'W', AND (JOBU = 'U' AND JOBT = 'T' AND M = N), then V is used as workspace if the procedure replaces A with A^. In that case, [U] is computed in V as right singular vectors of A^ and then copied back to the U array. This 'W' option is just a reminder to the caller that in this case V is reserved as workspace of length N*N. same deal as U, they're twinsies 💅
    • If JOBV = 'N', V is not referenced, unless JOBT='T'.
  • LDV — INTEGER

The leading dimension of the array V, LDV >= 1. If JOBV = 'V' or 'J' or 'W', then LDV >= N. periodt 😤

  • CWORK — COMPLEX array, dimension (MAX(2,LWORK))

If the call to CGEJSV is a workspace query (indicated by LWORK=-1 or LRWORK=-1), then on exit CWORK(1) contains the required length of CWORK for the job parameters used in the call. bestie this is the "how much room do I actually need" text-back 📱

  • LWORK — INTEGER

Length of CWORK to confirm proper allocation of workspace. LWORK depends on the job, so like… pay attention, there are branches 🦋

  1. If only SIGMA is needed ( JOBU = 'N', JOBV = 'N' ) and 1.1. .. no scaled condition estimate required (JOBA.NE.'E'.AND.JOBA.NE.'G'): LWORK >= 2*N+1. This is the minimal requirement. ->> For optimal performance (blocked code) the optimal value is LWORK >= N + (N+1)*NB. Here NB is the optimal block size for CGEQP3 and CGEQRF. In general, optimal LWORK is computed as
LWORK >= max(N+LWORK(CGEQP3),N+LWORK(CGEQRF), LWORK(CGESVJ)).

1.2. .. an estimate of the scaled condition number of A is required (JOBA='E', or 'G'). In this case, the minimal requirement is LWORK >= N*N + 2*N. ->> For optimal performance (blocked code) the optimal value is LWORK >= max(N+(N+1)*NB, N*N+2*N)=N**2+2*N. In general, the optimal length LWORK is computed as

LWORK >= max(N+LWORK(CGEQP3),N+LWORK(CGEQRF), LWORK(CGESVJ),
             N*N+LWORK(CPOCON)).
  1. If SIGMA and the right singular vectors are needed (JOBV = 'V'), (JOBU = 'N') 2.1. .. no scaled condition estimate requested (JOBE = 'N'): -> the minimal requirement is LWORK >= 3*N. -> For optimal performance, LWORK >= max(N+(N+1)*NB, 2*N+N*NB)=2*N+N*NB, where NB is the optimal block size for CGEQP3, CGEQRF, CGELQF, CUNMLQ. In general, the optimal length LWORK is computed as
LWORK >= max(N+LWORK(CGEQP3), N+LWORK(CGESVJ),
        N+LWORK(CGELQF), 2*N+LWORK(CGEQRF), N+LWORK(CUNMLQ)).

2.2. .. an estimate of the scaled condition number of A is required (JOBA='E', or 'G'). -> the minimal requirement is LWORK >= 3*N. -> For optimal performance, LWORK >= max(N+(N+1)*NB, 2*N,2*N+N*NB)=2*N+N*NB, where NB is the optimal block size for CGEQP3, CGEQRF, CGELQF, CUNMLQ. In general, the optimal length LWORK is computed as

LWORK >= max(N+LWORK(CGEQP3), LWORK(CPOCON), N+LWORK(CGESVJ),
        N+LWORK(CGELQF), 2*N+LWORK(CGEQRF), N+LWORK(CUNMLQ)).
  1. If SIGMA and the left singular vectors are needed 3.1. .. no scaled condition estimate requested (JOBE = 'N'): -> the minimal requirement is LWORK >= 3*N. -> For optimal performance: if JOBU = 'U' :: LWORK >= max(3*N, N+(N+1)*NB, 2*N+N*NB)=2*N+N*NB, where NB is the optimal block size for CGEQP3, CGEQRF, CUNMQR. In general, the optimal length LWORK is computed as
LWORK >= max(N+LWORK(CGEQP3), 2*N+LWORK(CGEQRF), N+LWORK(CUNMQR)).

3.2. .. an estimate of the scaled condition number of A is required (JOBA='E', or 'G'). -> the minimal requirement is LWORK >= 3*N. -> For optimal performance: if JOBU = 'U' :: LWORK >= max(3*N, N+(N+1)*NB, 2*N+N*NB)=2*N+N*NB, where NB is the optimal block size for CGEQP3, CGEQRF, CUNMQR. In general, the optimal length LWORK is computed as

LWORK >= max(N+LWORK(CGEQP3),N+LWORK(CPOCON),
         2*N+LWORK(CGEQRF), N+LWORK(CUNMQR)).
  1. If the full SVD is needed: (JOBU = 'U' or JOBU = 'F') and 4.1. if JOBV = 'V' the minimal requirement is LWORK >= 5*N+2*N*N. 4.2. if JOBV = 'J' the minimal requirement is LWORK >= 4*N+N*N.

In both cases, the allocated CWORK can accommodate blocked runs of CGEQP3, CGEQRF, CGELQF, CUNMQR, CUNMLQ. ate and left no crumbs 🔥

If the call to CGEJSV is a workspace query (indicated by LWORK=-1 or LRWORK=-1), then on exit CWORK(1) contains the optimal and CWORK(2) contains the minimal length of CWORK for the job parameters used in the call. two numbers, one array, iconic behavior 👑

  • RWORK — REAL array, dimension (MAX(7,LRWORK))

On exit, the receipts are as follows 📋

  • RWORK(1) = Determines the scaling factor SCALE = RWORK(2) / RWORK(1) such that SCALE*SVA(1:N) are the computed singular values of A. (See the description of SVA().)
  • RWORK(2) = See the description of RWORK(1). (yes it's a duo, they come as a set 💖)
  • RWORK(3) = SCONDA is an estimate for the condition number of column equilibrated A. (If JOBA = 'E' or 'G') SCONDA is an estimate of SQRT(||(R^* * R)^(-1)||_1). It is computed using CPOCON. It holds
N^(-1/4) * SCONDA <= ||R^(-1)||_2 <= N^(1/4) * SCONDA

where R is the triangular factor from the QRF of A. However — listen — if R is truncated and the numerical rank is determined to be strictly smaller than N, SCONDA is returned as -1, thus indicating that the smallest singular values might be lost 😭 that's the sad part, don't ignore it.

If full SVD is needed, the following two condition numbers are useful for the analysis of the algorithm. They are provided for a developer/implementer who is familiar with the details of the method. (iykyk 🧚)

  • RWORK(4) = an estimate of the scaled condition number of the triangular factor in the first QR factorization.
  • RWORK(5) = an estimate of the scaled condition number of the triangular factor in the second QR factorization.

The following two parameters are computed if JOBT = 'T'. They are provided for a developer/implementer who is familiar with the details of the method.

  • RWORK(6) = the entropy of A^* * A :: this is the Shannon entropy of diag(A^* * A) / Trace(A^* * A) taken as point in the probability simplex. entropy?? in my SVD?? it's more likely than you think 🌈
  • RWORK(7) = the entropy of A * A^*. (See the description of RWORK(6).)

If the call to CGEJSV is a workspace query (indicated by LWORK=-1 or LRWORK=-1), then on exit RWORK(1) contains the required length of RWORK for the job parameters used in the call. same energy as CWORK(1), just on the real side ⭐

  • LRWORK

🧮 LRWORK

LRWORK is INTEGER — it carries the length of RWORK, and honestly its whole personality is "confirm you allocated your workspace correctly, bestie." It depends on the job, and there are four whole situations:

  1. Only the singular values are requested, i.e. if
LSAME(JOBU,'N') .AND. LSAME(JOBV,'N')

then:

1.1. If LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G'), then: LRWORK = max( 7, 2 * M ).

1.2. Otherwise, LRWORK = max( 7, N ).

  1. Singular values with the right singular vectors are requested, i.e. if
(LSAME(JOBV,'V').OR.LSAME(JOBV,'J')) .AND.
.NOT.(LSAME(JOBU,'U').OR.LSAME(JOBU,'F'))

then:

2.1. If LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G'), then LRWORK = max( 7, 2 * M ).

2.2. Otherwise, LRWORK = max( 7, N ).

  1. Singular values with the left singular vectors are requested, i.e. if
(LSAME(JOBU,'U').OR.LSAME(JOBU,'F')) .AND.
.NOT.(LSAME(JOBV,'V').OR.LSAME(JOBV,'J'))

then:

3.1. If LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G'), then LRWORK = max( 7, 2 * M ).

3.2. Otherwise, LRWORK = max( 7, N ).

  1. Singular values with both the left and the right singular vectors are requested, i.e. if
(LSAME(JOBU,'U').OR.LSAME(JOBU,'F')) .AND.
(LSAME(JOBV,'V').OR.LSAME(JOBV,'J'))

then:

4.1. If LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G'), then LRWORK = max( 7, 2 * M ).

4.2. Otherwise, LRWORK = max( 7, N ).

🫶 And if, on entry, LRWORK = -1 or LWORK=-1, then a workspace query is assumed, and the length of RWORK gets returned in RWORK(1). It's giving "just asking politely, not actually doing the work." ✨

🗂️ IWORK

IWORK is an INTEGER array, dimension at least 4, and it also further depends on the job. Like, of course it does. 💅

  1. Only the singular values are requested, then: if ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) then the length of IWORK is N+M; otherwise the length of IWORK is N.
  2. The singular values and the right singular vectors are requested, then: if ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) then the length of IWORK is N+M; otherwise the length of IWORK is N.
  3. The singular values and the left singular vectors are requested, then: if ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) then the length of IWORK is N+M; otherwise the length of IWORK is N.
  4. The singular values with both the left and the right singular vectors are requested, then:

4.1. If LSAME(JOBV,'J') the length of IWORK is determined as follows: if ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) then the length of IWORK is N+M; otherwise the length of IWORK is N.

4.2. If LSAME(JOBV,'V') the length of IWORK is determined as follows: if ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) then the length of IWORK is 2*N+M; otherwise the length of IWORK is 2*N.

On exit, the receipts are:

  • IWORK(1) = the numerical rank determined after the initial QR factorization with pivoting. See the descriptions of JOBA and JOBR.
  • IWORK(2) = the number of the computed nonzero singular values
  • IWORK(3) = if nonzero, a warning message: if IWORK(3) = 1 then some of the column norms of A were denormalized floats. The requested high accuracy is not warranted by the data.
  • IWORK(4) = 1 or -1. If IWORK(4) = 1, then the procedure used A^* to do the job as specified by the JOB parameters.
⚠️ And if the call to CGEJSV is a workspace query (indicated by LWORK = -1 and LRWORK = -1), then on exit IWORK(1) contains the required length of IWORK for the job parameters used in the call. So yeah, the exit value is doing double duty — read the room before you trust it. 🔥

🚦 INFO

INFO is INTEGER, and it's the vibe check at the end of the call:

  • < 0: if INFO = -i, then the i-th argument had an illegal value. Not me getting yelled at by an index. 💀
  • = 0: successful exit;
  • > 0: CGEJSV did not converge in the maximal allowed number of sweeps. The computed values may be inaccurate.
😭 That last one is the "girl, it tried" exit code. Your numbers are still there, they're just suspect.

✍️ Author

Univ. of Tennessee

Univ. of California Berkeley

Univ. of Colorado Denver

NAG Ltd.

🔬 Further Details:

CGEJSV implements a preconditioned Jacobi SVD algorithm. It uses CGEQP3, CGEQRF, and CGELQF as preprocessors and preconditioners. Optionally, an additional row pivoting can be used as a preprocessor, which in some cases results in much higher accuracy. An example is matrix A with the structure A = D1 * C * D2, where D1, D2 are arbitrarily ill-conditioned diagonal matrices and C is well-conditioned matrix. In that case, complete pivoting in the first QR factorizations provides accuracy dependent on the condition number of C, and independent of D1, D2. Such higher accuracy is not completely understood theoretically, but it works well in practice. Further, if A can be written as A = B*D, with well-conditioned B and some diagonal D, then the high accuracy is guaranteed, both theoretically and in software, independent of D. For more details see [1], [2].

The computational range for the singular values can be the full range ( UNDERFLOW,OVERFLOW ), provided that the machine arithmetic and the BLAS & LAPACK routines called by CGEJSV are implemented to work in that range. If that is not the case, then the restriction for safe computation with the singular values in the range of normalized IEEE numbers is that the spectral condition number kappa(A)=sigma_max(A)/sigma_min(A) does not overflow. This code (CGEJSV) is best used in this restricted range, meaning that singular values of magnitude below ||A||_2 / SLAMCH('O') are returned as zeros. See JOBR for details on this.

Further, this implementation is somewhat slower than the one described in [1,2] due to replacement of some non-LAPACK components, and because the choice of some tuning parameters in the iterative part (CGESVJ) is left to the implementer on a particular machine.

The rank revealing QR factorization (in this code: CGEQP3) should be implemented as in [3]. We have a new version of CGEQP3 under development that is more robust than the current one in LAPACK, with a cleaner cut in rank deficient cases. It will be available in the SIGMA library [4]. If M is much larger than N, it is obvious that the initial QRF with column pivoting can be preprocessed by the QRF without pivoting. That well known trick is not used in CGEJSV because in some cases heavy row weighting can be treated with complete pivoting. The overhead in cases M much larger than N is then only due to pivoting, but the benefits in terms of accuracy have prevailed. The implementer/user can incorporate this extra QRF step easily. The implementer can also improve data movement (matrix transpose, matrix copy, matrix transposed copy) — this implementation of CGEJSV uses only the simplest, naive data movement.

💀 "naive data movement" said with love. It's honest work, bestie.

🫶 Contributor:

Zlatko Drmac (Zagreb, Croatia)

📚 References:

[1] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm I. SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1322-1342. LAPACK Working note 169. [2] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm II. SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1343-1362. LAPACK Working note 170. [3] Z. Drmac and Z. Bujanovic: On the failure of rank-revealing QR factorization software - a case study. ACM Trans. Math. Softw. Vol. 35, No 2 (2008), pp. 1-28. LAPACK Working note 176. [4] Z. Drmac: SIGMA - mathematical software library for accurate SVD, PSV, QSVD, (H,K)-SVD computations. Department of Mathematics, University of Zagreb, 2008, 2016.

🐛 Bugs, examples and comments:

Please report all bugs and send interesting examples and/or comments to drmac@math.hr. Thank you.

🦋 Like, actually send the weird ones. They want the weird ones. Interesting examples are the whole point, iykyk.

Definition at line 565 of file cgejsv.f.

Author — take two 🎬

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