/home/abui...gedmdq.f90(3)

📦 version 3.12.0, in opensuse-leap 16.0

ZGEDMDQ 💅

okay so this is the Library Functions Manual page for the file right below, and it's giving “LAPACK, but make it Dynamic Mode Decomposition” energy. no cap, it's a whole subroutine situation.

🌸 NAME 🌸

/home/abuild/rpmbuild/BUILD/lapack-static-3.12.0-build/lapack-3.12.0/SRC/zgedmdq.f90

✨ SYNOPSIS ✨

Functions/Subroutines

subroutine ZGEDMDQ (jobs, jobz, jobr, jobq, jobt, jobf, whtsvd, m, n, f,
   ldf, x, ldx, y, ldy, nrnk, tol, k, eigs, z, ldz, res, b, ldb, v,
   ldv, s, lds, zwork, lzwork, work, lwork, iwork, liwork, info)

ZGEDMDQ computes the Dynamic Mode Decomposition (DMD) for a pair of data snapshot matrices — bestie, that's literally the whole vibe.

💖 Function/Subroutine Documentation 💖

subroutine ZGEDMDQ (character, intent(in) jobs, character, intent(in) jobz,
   character, intent(in) jobr, character, intent(in) jobq, character,
   intent(in) jobt, character, intent(in) jobf, integer, intent(in) whtsvd,
   integer, intent(in) m, integer, intent(in) n, complex(kind=wp),
   dimension(ldf,*), intent(inout) f, integer, intent(in) ldf,
   complex(kind=wp), dimension(ldx,*), intent(out) x, integer, intent(in)
   ldx, complex(kind=wp), dimension(ldy,*), intent(out) y, integer,
   intent(in) ldy, integer, intent(in) nrnk, real(kind=wp), intent(in) tol,
   integer, intent(out) k, complex(kind=wp), dimension(*), intent(out)
   eigs, complex(kind=wp), dimension(ldz,*), intent(out) z, integer,
   intent(in) ldz, real(kind=wp), dimension(*), intent(out) res,
   complex(kind=wp), dimension(ldb,*), intent(out) b, integer, intent(in)
   ldb, complex(kind=wp), dimension(ldv,*), intent(out) v, integer,
   intent(in) ldv, complex(kind=wp), dimension(lds,*), intent(out) s,
   integer, intent(in) lds, complex(kind=wp), dimension(*), intent(out)
   zwork, integer, intent(in) lzwork, real(kind=wp), dimension(*),
   intent(out) work, integer, intent(in) lwork, integer, dimension(*),
   intent(out) iwork, integer, intent(in) liwork, integer, intent(out)
   info)

ZGEDMDQ computes the Dynamic Mode Decomposition (DMD) for a pair of data snapshot matrices. iconic behavior, honestly.

🦋 Purpose 🦋

okay so here's the tea: ZGEDMDQ computes the Dynamic Mode Decomposition (DMD) for a pair of data snapshot matrices, using a QR factorization based compression of the data. For the input matrices X and Y such that Y = A*X with an unaccessible matrix A, ZGEDMDQ computes a certain number of Ritz pairs of A using the standard Rayleigh-Ritz extraction from a subspace of range(X) that is determined using the leading left singular vectors of X.

Optionally, ZGEDMDQ returns the residuals of the computed Ritz pairs, the information needed for a refinement of the Ritz vectors, or the eigenvectors of the Exact DMD. For further details see the references listed below. For more details of the implementation see [3]. periodt.

📚 References 📚

  • [1] P. Schmid: Dynamic mode decomposition of numerical and experimental data, Journal of Fluid Mechanics 656, 5-28, 2010.
  • [2] Z. Drmac, I. Mezic, R. Mohr: Data driven modal decompositions: analysis and enhancements, SIAM J. on Sci. Comp. 40 (4), A2253-A2285, 2018.
  • [3] Z. Drmac: A LAPACK implementation of the Dynamic Mode Decomposition I. Technical report. AIMDyn Inc. and LAPACK Working Note 298.
  • [4] J. Tu, C. W. Rowley, D. M. Luchtenburg, S. L. Brunton, N. Kutz: On Dynamic Mode Decomposition: Theory and Applications, Journal of Computational Dynamics 1(2), 391 -421, 2014.

💖 Developed and supported by 💖

Developed and coded by Zlatko Drmac, Faculty of Science, University of Zagreb; drmac@math.hr

In cooperation with AIMdyn Inc., Santa Barbara, CA.

and supported by:

  • DARPA SBIR project — Contract No: W31P4Q-21-C-0007
  • DARPA PAI project — Contract No: HR0011-18-9-0033
  • DARPA MoDyL project — Contract No: HR0011-16-C-0116

Any opinions, findings and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the DARPA SBIR Program Office.

💖 Developed and supported by (the sequel) 💖

Distribution Statement A: Approved for Public Release, Distribution Unlimited.

Cleared by DARPA on September 29, 2022.

🎀 Parameters 🎀

JOBS

JOBS (input) CHARACTER*1 — determines whether the initial data snapshots are scaled by a diagonal matrix. The data snapshots are the columns of F. The leading N-1 columns of F are denoted X and the trailing N-1 columns are denoted Y.

  • 'S' — the data snapshots matrices X and Y are multiplied with a diagonal matrix D so that X*D has unit nonzero columns (in the Euclidean 2-norm)
  • 'C' — the snapshots are scaled as with the 'S' option. If it is found that an i-th column of X is zero vector and the corresponding i-th column of Y is non-zero, then the i-th column of Y is set to zero and a warning flag is raised.
  • 'Y' — the data snapshots matrices X and Y are multiplied by a diagonal matrix D so that Y*D has unit nonzero columns (in the Euclidean 2-norm)
  • 'N' — no data scaling.

JOBZ

JOBZ (input) CHARACTER*1 — determines whether the eigenvectors (Koopman modes) will be computed.

  • 'V' — the eigenvectors (Koopman modes) will be computed and returned in the matrix Z. See the description of Z.
  • 'F' — the eigenvectors (Koopman modes) will be returned in factored form as the product Z*V, where Z is orthonormal and V contains the eigenvectors of the corresponding Rayleigh quotient. See the descriptions of F, V, Z.
  • 'Q' — the eigenvectors (Koopman modes) will be returned in factored form as the product Q*Z, where Z contains the eigenvectors of the compression of the underlying discretized operator onto the span of the data snapshots. See the descriptions of F, V, Z. Q is from the initial QR factorization.
  • 'N' — the eigenvectors are not computed.

JOBR

JOBR (input) CHARACTER*1 — determines whether to compute the residuals.

  • 'R' — the residuals for the computed eigenpairs will be computed and stored in the array RES. See the description of RES.
  • 'N' — the residuals are not computed.
bestie, heads up: for the 'R' option to be legal, JOBZ must be 'V'. no sneaky shortcuts.

JOBQ

JOBQ (input) CHARACTER*1 — specifies whether to explicitly compute and return the unitary matrix from the QR factorization.

  • 'Q' — the matrix Q of the QR factorization of the data snapshot matrix is computed and stored in the array F. See the description of F.
  • 'N' — the matrix Q is not explicitly computed.

JOBT

JOBT (input) CHARACTER*1 — specifies whether to return the upper triangular factor from the QR factorization.

  • 'R' — the matrix R of the QR factorization of the data snapshot matrix F is returned in the array Y. See the description of Y and Further details.
  • 'N' — the matrix R is not returned.

JOBF

JOBF (input) CHARACTER*1 — specifies whether to store information needed for post-processing (e.g. computing refined Ritz vectors).

  • 'R' — the matrix needed for the refinement of the Ritz vectors is computed and stored in the array B. See the description of B.
  • 'E' — the unscaled eigenvectors of the Exact DMD are computed and returned in the array B. See the description of B.
  • 'N' — no eigenvector refinement data is computed.
to be useful on exit, this option needs JOBQ='Q'. iykyk.

WHTSVD

WHTSVD — WHTSVD (input) INTEGER, WHSTVD in { 1, 2, 3, 4 } — allows for a selection of the SVD algorithm from the LAPACK library. yes, the source really says WHSTVD there; we're keeping it exactly as-is, bestie.

  • 1 :: ZGESVD (the QR SVD algorithm)
  • 2 :: ZGESDD (the Divide and Conquer algorithm; if enough workspace available, this is the fastest option)
  • 3 :: ZGESVDQ (the preconditioned QR SVD ; this and 4 are the most accurate options)
  • 4 :: ZGEJSV (the preconditioned Jacobi SVD; this and 3 are the most accurate options)

For the four methods above, a significant difference in the accuracy of small singular values is possible if the snapshots vary in norm so that X is severely ill-conditioned. If small (smaller than EPS*||X||) singular values are of interest and JOBS=='N', then the options (3, 4) give the most accurate results, where the option 4 is slightly better and with stronger theoretical background. If JOBS=='S', i.e. the columns of X will be normalized, then all methods give nearly equally accurate results.

M

M (input) INTEGER, M >= 0 — the state space dimension (the number of rows of F).

N

N (input) INTEGER, 0 <= N <= M — the number of data snapshots from a single trajectory, taken at equidistant discrete times. This is the number of columns of F.

F

F (input/output) COMPLEX(KIND=WP) M-by-N array.

On entry: the columns of F are the sequence of data snapshots from a single trajectory, taken at equidistant discrete times. It is assumed that the column norms of F are in the range of the normalized floating point numbers.
On exit: if JOBQ == 'Q', the array F contains the orthogonal matrix/factor of the QR factorization of the initial data snapshots matrix F. See the description of JOBQ. If JOBQ == 'N', the entries in F strictly below the main diagonal contain, column-wise, the information on the Householder vectors, as returned by ZGEQRF. The remaining information to restore the orthogonal matrix of the initial QR factorization is stored in ZWORK(1:MIN(M,N)). See the description of ZWORK.

LDF

  • LDF — input INTEGER, and it has to satisfy LDF >= M. It's the leading dimension of array F, periodt 💅.
  • X — workspace/output COMPLEX(KIND=WP) with dimensions MIN(M,N)-by-(N-1). Bestie, X is the workspace holding representations of the leading N-1 snapshots in the orthonormal basis computed in the QR factorization of F. On exit, the leading K columns of X contain the leading K left singular vectors of the above described content of X. To lift them to the space of the left singular vectors U(:,1:K) of the input data, pre-multiply with the Q factor from the initial QR factorization. See the descriptions of F, K, V and Z. 🦋
  • LDX — input INTEGER, LDX >= N. It's the leading dimension of the array X, no cap ✨.
  • Y — workspace/output COMPLEX(KIND=WP) with dimensions MIN(M,N)-by-(N). Y is used as workspace to hold representations of the trailing N-1 snapshots in the orthonormal basis computed in the QR factorization of F. On exit, if JOBT == 'R', Y contains the MIN(M,N)-by-N upper triangular factor from the QR factorization of the data snapshot matrix F 👑.
  • LDY — input INTEGER, LDY >= N. It's the leading dimension of the array Y, slay 💅.
  • NRNK — input INTEGER. It determines the mode for computing the numerical rank, i.e. how to truncate small singular values of the input matrix X. On input, if:
    • NRNK = -1 :: the i-th singular value sigma(i) is truncated if sigma(i) <= TOL*sigma(1). This option is recommended 💖.
    • NRNK = -2 :: the i-th singular value sigma(i) is truncated if sigma(i) <= TOL*sigma(i-1). This option is included for R&D purposes. It requires highly accurate SVD, which may not be feasible, bestie 😭.
    • The numerical rank can be enforced by using a positive value of NRNK as follows: 0 < NRNK <= N-1 :: at most NRNK largest singular values will be used. If the number of the computed nonzero singular values is less than NRNK, then only those nonzero values will be used and the actually used dimension is less than NRNK. The actual number of the nonzero singular values is returned in the variable K. See the description of K.
  • TOL — input REAL(KIND=WP), 0 <= TOL < 1. It's the tolerance for truncating small singular values. See the description of NRNK 🌸.
  • K — output INTEGER, 0 <= K <= N. It's the dimension of the SVD/POD basis for the leading N-1 data snapshots (columns of F) and the number of the computed Ritz pairs. The value of K is determined according to the rule set by the parameters NRNK and TOL. See the descriptions of NRNK and TOL ✨.
  • EIGS — output COMPLEX(KIND=WP) with dimensions (N-1)-by-1. The leading K (K<=N-1) entries of EIGS contain the computed eigenvalues (Ritz values). See the descriptions of K, and Z 🎀.
  • Z — workspace/output COMPLEX(KIND=WP) with dimensions M-by-(N-1). If JOBZ =='V' then Z contains the Ritz vectors. Z(:,i) is an eigenvector of the i-th Ritz value; ||Z(:,i)||_2=1. If JOBZ == 'F', then the Z(:,i)'s are given implicitly as Z*V, where Z contains the orthonormal matrix (the product of Q from the initial QR factorization and the SVD/POD_basis returned by ZGEDMD in X) and the second factor (the eigenvectors of the Rayleigh quotient) is in the array V, as returned by ZGEDMD. That is, X(:,1:K)*V(:,i) is an eigenvector corresponding to EIGS(i). The columns of V(1:K,1:K) are the computed eigenvectors of the K-by-K Rayleigh quotient. See the descriptions of EIGS, X and V 🧚.
  • LDZ — input INTEGER, LDZ >= M. It's the leading dimension of the array Z, periodt 💜.
  • RES — output REAL(KIND=WP) with dimensions (N-1)-by-1. RES(1:K) contains the residuals for the K computed Ritz pairs, RES(i) = || A * Z(:,i) - EIGS(i)*Z(:,i))||_2. See the description of EIGS and Z 🔥.
  • B — output COMPLEX(KIND=WP) with dimensions MIN(M,N)-by-(N-1).
    • If JOBF =='R', B(1:N,1:K) contains A*U(:,1:K), and can be used for computing the refined vectors; see further details in the provided references.
    • If JOBF == 'E', B(1:N,1;K) contains A*U(:,1:K)*W(1:K,1:K), which are the vectors from the Exact DMD, up to scaling by the inverse eigenvalues.
    • In both cases, the content of B can be lifted to the original dimension of the input data by pre-multiplying with the Q factor from the initial QR factorization. Here A denotes a compression of the underlying operator. See the descriptions of F and X.
    • If JOBF =='N', then B is not referenced 🙈.
  • LDB — input INTEGER, LDB >= MIN(M,N). It's the leading dimension of the array B, bestie 💅.
  • V — workspace/output COMPLEX(KIND=WP) with dimensions (N-1)-by-(N-1). On exit, V(1:K,1:K) V contains the K eigenvectors of the Rayleigh quotient. The Ritz vectors (returned in Z) are the product of Q from the initial QR factorization (see the description of F), X (see the description of X) and V 🌈.
  • LDV — input INTEGER, LDV >= N-1. It's the leading dimension of the array V ⭐.
  • S — output COMPLEX(KIND=WP) with dimensions (N-1)-by-(N-1). The array S(1:K,1:K) is used for the matrix Rayleigh quotient. This content is overwritten during the eigenvalue decomposition by ZGEEV. See the description of K 🍓.
  • LDS — input INTEGER, LDS >= N-1. It's the leading dimension of the array S 💖.
  • LZWORK (first block — yep, it's actually describing ZWORK) — ZWORK is workspace/output COMPLEX(KIND=WP) with dimensions LWORK-by-1. On exit, ZWORK(1:MIN(M,N)) contains the scalar factors of the elementary reflectors as returned by ZGEQRF of the M-by-N input matrix F. If the call to ZGEDMDQ is only workspace query, then ZWORK(1) contains the minimal complex workspace length and ZWORK(2) is the optimal complex workspace length. Hence, the length of work is at least 2. See the description of LZWORK 💀.
  • LZWORK — input INTEGER. The minimal length of the workspace vector ZWORK. LZWORK is calculated as follows:
Let MLWQR  = N (minimal workspace for ZGEQRF[M,N])
    MLWDMD = minimal workspace for ZGEDMD (see the
             description of LWORK in ZGEDMD)
    MLWMQR = N (minimal workspace for
               ZUNMQR['L','N',M,N,N])
    MLWGQR = N (minimal workspace for ZUNGQR[M,N,N])
    MINMN  = MIN(M,N)
Then
LZWORK = MAX(2, MIN(M,N)+MLWQR, MINMN+MLWDMD)
is further updated as follows:
   if   JOBZ == 'V' or JOBZ == 'F' THEN
        LZWORK = MAX(LZWORK, MINMN+MLWMQR)
   if   JOBQ == 'Q' THEN
        LZWORK = MAX(ZLWORK, MINMN+MLWGQR)
  • WORK — workspace/output REAL(KIND=WP) with dimensions LWORK-by-1. On exit, WORK(1:N-1) contains the singular values of the input submatrix F(1:M,1:N-1). If the call to ZGEDMDQ is only workspace query, then WORK(1) contains the minimal workspace length and WORK(2) is the optimal workspace length. hence, the length of work is at least 2. See the description of LWORK 🫶.
  • LWORK — input INTEGER. The minimal length of the workspace vector WORK. LWORK is the same as in ZGEDMD, because in ZGEDMDQ only ZGEDMD requires real workspace for snapshots of dimensions MIN(M,N)-by-(N-1). If on entry LWORK = -1, then a workspace query is assumed and the procedure only computes the minimal and the optimal workspace length for WORK 😭.
  • IWORK — workspace/output INTEGER with dimensions LIWORK-by-1. Workspace that is required only if WHTSVD equals 2 , 3 or 4. (See the description of WHTSVD.) If on entry LWORK =-1 or LIWORK=-1, then the minimal length of IWORK is computed and returned in IWORK(1). See the description of LIWORK 💜.
  • LIWORK — input INTEGER. The minimal length of the workspace vector IWORK. If WHTSVD == 1, then only IWORK(1) is used; LIWORK >=1. Let M1=MIN(M,N), N1=N-1. Then:
    • If WHTSVD == 2, then LIWORK >= MAX(1,8*MIN(M1,N1))
    • If WHTSVD == 3, then LIWORK >= MAX(1,M1+N1-1)
    • If WHTSVD == 4, then LIWORK >= MAX(3,M1+3*N1)
    • If on entry LIWORK = -1, then a workspace query is assumed and the procedure only computes the minimal and the optimal workspace lengths for both WORK and IWORK. See the descriptions of WORK and IWORK 🌟.
  • INFO — and this part literally stops at INFO with no description after it, so I'm not inventing deets. Not me making up lore 🙈💀.
  • INFO (output) INTEGER — the receipts bestie, this is how you find out what actually went down 👀
    • -i < 0 — on entry, the i-th argument had an illegal value 🙈 like, that arg was not it
    • = 0 — successful return, we ate, no notes ✨
    • = 1 — void input, quick exit (M=0 or N=0), literally nothing to do here 💀
    • = 2 — the SVD computation of X did not converge 😭 suggestion: check the input data and/or repeat with a different WHTSVD
    • = 3 — the computation of the eigenvalues did not converge, nope, not today 🫠
    • = 4 — if data scaling was requested on input and the procedure found inconsistency in the data such that for some column index i, X(:,i) = 0 but Y(:,i) /= 0, then Y(:,i) is set to zero if JOBS=='C'. The computation proceeds with original or modified data and the warning flag is set with INFO=4 ⚠️
ok so lowkey INFO=4 is the "we fixed it but we're telling you anyway" vibe — the run keeps going, nothing crashes, but you're on notice, periodt 💅

🫶 AUTHOR 🫶

Zlatko Drmac — the icon who wrote this, we stan 👑

Definition at line 550 of file zgedmdq.f90. 📍

🤖 AUTHOR (the meta one) 🤖

Generated automatically by Doxygen for LAPACK from the source code — so like, the robots made this page, not a human, no cap 🦋

and that's a wrap on the whole thing bestie!! 🎀 this cutie came from LAPACK, Version 3.12.0, straight out of /home/abui...gedmdq.f90(3) — go forth and slay your eigenvalue problems 💖✨👑

📦 where this page comes from