SRC/cgedmd.f90(3)

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version 3.12.1 · 4 versions
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💖 SRC/cgedmd.f90(3) 💖

(from the Library Functions Manual — yes bestie, that's the manual name up top)

🌸 NAME 🌸

SRC/cgedmd.f90

✨ SYNOPSIS ✨

🎀 Functions/Subroutines 🎀

Here's the call, copied exactly because we do not mess with signatures:

subroutine cgedmd (jobs, jobz, jobr, jobf, whtsvd, m, n, x, ldx, y, ldy,
           nrnk, tol, k, eigs, z, ldz, res, b, ldb, w, ldw, s, lds, zwork,
           lzwork, rwork, lrwork, iwork, liwork, info)

CGEDMD computes the Dynamic Mode Decomposition (DMD) for a pair of data snapshot matrices. That's the whole synopsis vibe: snapshots in, DMD out, and honestly? Iconic.

🦋 Function/Subroutine Documentation 🦋

Okay so here's the full signature with all the intent tea, exactly as it is:

subroutine cgedmd (character, intent(in) jobs, character, intent(in) jobz,
       character, intent(in) jobr, character, intent(in) jobf, integer,
       intent(in) whtsvd, integer, intent(in) m, integer, intent(in) n,
       complex(kind=wp), dimension(ldx,*), intent(inout) x, integer, intent(in)
       ldx, complex(kind=wp), dimension(ldy,*), intent(inout) 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(ldw,*), intent(out) w, integer,
       intent(in) ldw, 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) rwork, integer, intent(in) lrwork, integer, dimension(*),
       intent(out) iwork, integer, intent(in) liwork, integer, intent(out)
       info)

CGEDMD computes the Dynamic Mode Decomposition (DMD) for a pair of data snapshot matrices.

✨ Purpose ✨

Okay bestie, the purpose is this: CGEDMD computes the Dynamic Mode Decomposition (DMD) for a pair of data snapshot matrices. For the input matrices X and Y such that Y = A*X with an unaccessible matrix A, CGEDMD 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, CGEDMD 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].

📚 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.

🌈 Distribution Statement A 🌈

Approved for Public Release, Distribution Unlimited. Cleared by DARPA on September 29, 2022

🧚 Parameters 🧚

JOBS

JOBS (input) CHARACTER*1 — tells us whether the initial data snapshots get scaled by a diagonal matrix. Yes bestie, it's the scaling vibe check.

  • '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. Simple. Clean. We respect it.

JOBZ

JOBZ (input) CHARACTER*1 — decides 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 X(:,1:K)*W, where X contains a POD basis (leading left singular vectors of the data matrix X) and W contains the eigenvectors of the corresponding Rayleigh quotient. See the descriptions of K, X, W, Z.
  • 'N' — the eigenvectors are not computed.

JOBR

JOBR (input) CHARACTER*1 — decides 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. For this option to be legal, JOBZ must be 'V'.
  • 'N' — the residuals are not computed.
💀 caveat: for 'R' to be legal, JOBZ must be 'V'.

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.

WHTSVD

WHTSVD (input) INTEGER, WHSTVD in { 1, 2, 3, 4 } — okay the original really says WHSTVD there, bestie, keep it. This lets you select the SVD algorithm from the LAPACK library.

  • 1 — CGESVD (the QR SVD algorithm)
  • 2 — CGESDD (the Divide and Conquer algorithm; if enough workspace available, this is the fastest option)
  • 3 — CGESVDQ (the preconditioned QR SVD ; this and 4 are the most accurate options)
  • 4 — CGEJSV (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 row dimension of X, Y).

N

N (input) INTEGER, 0 <= N <= M — the number of data snapshot pairs (the number of columns of X and Y).

X

X (input/output) COMPLEX(KIND=WP) M-by-N array — bestie, this one is doing double duty.

  • > On entry, X contains the data snapshot matrix X. It is assumed that the column norms of X are in the range of the normalized floating point numbers.
  • < On exit, the leading K columns of X contain a POD basis, i.e. the leading K left singular vectors of the input data matrix X, U(:,1:K). All N columns of X contain all left singular vectors of the input matrix X. See the descriptions of K, Z and W.

LDX

LDX (input) INTEGER, LDX >= M — the leading dimension of the array X.

Y

Y (input/workspace/output) COMPLEX(KIND=WP) M-by-N array — this one is input, workspace, and output, a whole main character.

  • > On entry, Y contains the data snapshot matrix Y.
  • < On exit:
    • If JOBR == 'R', the leading K columns of Y contain the residual vectors for the computed Ritz pairs. See the description of RES.
    • If JOBR == 'N', Y contains the original input data, scaled according to the value of JOBS.

LDY

LDY (input) INTEGER , LDY >= M — the leading dimension of the array Y.

NRNK

NRNK (input) INTEGER — determines the mode how to compute the numerical rank, i.e. how to truncate small singular values of the input matrix X. On input, if:

  • NRNK = -1 — i-th singular value sigma(i) is truncated if sigma(i) <= TOL*sigma(1). This option is recommended.
  • NRNK = -2 — 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.

The numerical rank can be enforced by using positive value of NRNK as follows:

  • 0 < NRNK <= N — 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 descriptions of TOL and K.

TOL

(the TOL tea continues in the next chunk, bestie — hold that thought)

🌸 Argument lore, continued 🌸

Picking up right at TOL — no cap, the parameter tea continues 💅

  • TOL — (input) REAL(KIND=WP), 0 <= TOL < 1. The tolerance for truncating small singular values. See the description of NRNK. That’s the whole vibe.
  • K — (output) INTEGER, 0 <= K <= N. This is the dimension of the POD basis for the data snapshot matrix X 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) N-by-1 array. The leading K (K<=N) entries of EIGS contain the computed eigenvalues (Ritz values). See the descriptions of K, and Z.
  • Z — (workspace/output) COMPLEX(KIND=WP) M-by-N array. 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 the columns of X(:,1:K)*W(1:K,1:K), i.e. X(:,1:K)*W(:,i) is an eigenvector corresponding to EIGS(i). The columns of W(1:k,1:K) are the computed eigenvectors of the K-by-K Rayleigh quotient. See the descriptions of EIGS, X and W.
  • LDZ — (input) INTEGER, LDZ >= M. The leading dimension of the array Z.
  • RES — (output) REAL(KIND=WP) N-by-1 array. 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) M-by-N array. If JOBF =='R', B(1:M,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:M,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. If JOBF =='N', then B is not referenced. See the descriptions of X, W, K.
  • LDB — (input) INTEGER, LDB >= M. The leading dimension of the array B.
  • W — (workspace/output) COMPLEX(KIND=WP) N-by-N array. On exit, W(1:K,1:K) contains the K computed eigenvectors of the matrix Rayleigh quotient. The Ritz vectors (returned in Z) are the product of X (containing a POD basis for the input matrix X) and W. See the descriptions of K, S, X and Z. W is also used as a workspace to temporarily store the right singular vectors of X.
  • LDW — (input) INTEGER, LDW >= N. The leading dimension of the array W.
  • S — (workspace/output) COMPLEX(KIND=WP) N-by-N array. The array S(1:K,1:K) is used for the matrix Rayleigh quotient. This content is overwritten during the eigenvalue decomposition by CGEEV. See the description of K.
  • LDS — (input) INTEGER, LDS >= N. The leading dimension of the array S.
  • ZWORK — (workspace/output) COMPLEX(KIND=WP) LZWORK-by-1 array. ZWORK is used as complex workspace in the complex SVD, as specified by WHTSVD (1,2, 3 or 4) and for CGEEV for computing the eigenvalues of a Rayleigh quotient. If the call to CGEDMD 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 MAX(LZWORK_SVD, LZWORK_CGEEV), where LZWORK_CGEEV = MAX( 1, 2*N ) and the minimal LZWORK_SVD is calculated as follows:
    • If WHTSVD == 1 :: CGESVD :: then LZWORK_SVD = MAX(1,2*MIN(M,N)+MAX(M,N))
    • If WHTSVD == 2 :: CGESDD :: then LZWORK_SVD = 2*MIN(M,N)*MIN(M,N)+2*MIN(M,N)+MAX(M,N)
    • If WHTSVD == 3 :: CGESVDQ :: then LZWORK_SVD = obtainable by a query
    • If WHTSVD == 4 :: CGEJSV :: then LZWORK_SVD = obtainable by a query

If on entry LZWORK = -1, then a workspace query is assumed and the procedure only computes the minimal and the optimal workspace lengths and returns them in LZWORK(1) and LZWORK(2), respectively.

  • RWORK — (workspace/output) REAL(KIND=WP) LRWORK-by-1 array. On exit, RWORK(1:N) contains the singular values of X (for JOBS=='N') or column scaled X (JOBS=='S', 'C'). If WHTSVD==4, then RWORK(N+1) and RWORK(N+2) contain scaling factor RWORK(N+2)/RWORK(N+1) used to scale X and Y to avoid overflow in the SVD of X. This may be of interest if the scaling option is off and as many as possible smallest eigenvalues are desired to the highest feasible accuracy. If the call to CGEDMD is only workspace query, then RWORK(1) contains the minimal workspace length. See the description of LRWORK.
  • LRWORK — (input) INTEGER. The minimal length of the workspace vector RWORK. LRWORK is calculated as follows: LRWORK = MAX(1, N+LRWORK_SVD,N+LRWORK_CGEEV), where LRWORK_CGEEV = MAX(1,2*N) and RWORK_SVD is the real workspace for the SVD subroutine determined by the input parameter WHTSVD.
    • If WHTSVD == 1 :: CGESVD :: then LRWORK_SVD = 5*MIN(M,N)
    • If WHTSVD == 2 :: CGESDD :: then LRWORK_SVD = MAX(5*MIN(M,N)*MIN(M,N)+7*MIN(M,N), 2*MAX(M,N)*MIN(M,N)+2*MIN(M,N)*MIN(M,N)+MIN(M,N) ) )
    • If WHTSVD == 3 :: CGESVDQ :: then LRWORK_SVD = obtainable by a query
    • If WHTSVD == 4 :: CGEJSV :: then LRWORK_SVD = obtainable by a query

If on entry LRWORK = -1, then a workspace query is assumed and the procedure only computes the minimal real workspace length and returns it in RWORK(1).

  • IWORK — (workspace/output) INTEGER LIWORK-by-1 array. 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. If WHTSVD == 2, then LIWORK >= MAX(1,8*MIN(M,N)). If WHTSVD == 3, then LIWORK >= MAX(1,M+N-1). If WHTSVD == 4, then LIWORK >= MAX(3,M+3*N). If on entry LIWORK = -1, then a workspace query is assumed and the procedure only computes the minimal and the optimal workspace lengths for ZWORK, RWORK and IWORK. See the descriptions of ZWORK, RWORK and IWORK.
  • INFO — (output) INTEGER. Here’s the status tea:
    • -i < 0 :: On entry, the i-th argument had an illegal value
    • = 0 :: Successful return.
    • = 1 :: Void input. Quick exit (M=0 or N=0).
    • = 2 :: The SVD computation of X did not converge. Suggestion: Check the input data and/or repeat with different WHTSVD.
    • = 3 :: The computation of the eigenvalues did not converge.
    • = 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 warning flag is set with INFO=4.
Bestie, INFO=4 is a warning-flag moment, not a silent vibe — the computation proceeds with original or modified data.

✨ Author ✨

Zlatko Drmac

Definition at line 496 of file cgedmd.f90.

🎀 Author (generated edition) 🎀

Generated automatically by Doxygen for LAPACK from the source code.

That’s the whole page, bestie. LAPACK, Version 3.12.0, page SRC/cgedmd.f90(3) — mwah 💖💅

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