SRC/sgedmd.f90(3)

NAME
SRC/sgedmd.f90

SYNOPSIS

  Functions/Subroutines
subroutine sgedmd (jobs, jobz, jobr, jobf, whtsvd, m, n, x, ldx, y, ldy, nrnk, tol, k, reig, imeig, z, ldz, res, b, ldb, w, ldw, s, lds, work, lwork, iwork, liwork, info)
SGEDMD computes the Dynamic Mode Decomposition (DMD) for a pair of data snapshot matrices.

Function/Subroutine Documentation

  subroutine sgedmd (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, real(kind=wp), dimension(ldx,*), intent(inout) x, integer, intent(in) ldx, real(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, real(kind=wp), dimension(*), intent(out) reig, real(kind=wp), dimension(*), intent(out) imeig, real(kind=wp), dimension(ldz,*), intent(out) z, integer, intent(in) ldz, real(kind=wp), dimension(*), intent(out) res, real(kind=wp), dimension(ldb,*), intent(out) b, integer, intent(in) ldb, real(kind=wp), dimension(ldw,*), intent(out) w, integer, intent(in) ldw, real(kind=wp), dimension(lds,*), intent(out) s, integer, intent(in) lds, real(kind=wp), dimension(*), intent(out) work, integer, intent(in) lwork, integer, dimension(*), intent(out) iwork, integer, intent(in) liwork, integer, intent(out) info)

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

Purpose:

        SGEDMD 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, SGEDMD 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, SGEDMD 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 'Koopman Operator-Based Forecasting
        for Nonstationary Processes from Near-Term, Limited
        Observational Data' Contract No: W31P4Q-21-C-0007
        - DARPA PAI project 'Physics-Informed Machine Learning
        Methodologies' Contract No: HR0011-18-9-0033
        - DARPA MoDyL project 'A Data-Driven, Operator-Theoretic
        Framework for Space-Time Analysis of Process Dynamics'
        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:

        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.
        '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 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
        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.
               For this option to be legal, JOBZ must be 'V'.
        'N' :: The residuals are not computed.

    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 }
        Allows for a selection of the SVD algorithm from the
        LAPACK library.
        1 :: SGESVD (the QR SVD algorithm)
        2 :: SGESDD (the Divide and Conquer algorithm; if enough
             workspace available, this is the fastest option)
        3 :: SGESVDQ (the preconditioned QR SVD  ; this and 4
             are the most accurate options)
        4 :: SGEJSV (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) REAL(KIND=WP) M-by-N array
        > 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) REAL(KIND=WP) M-by-N array
        > 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

        TOL (input) REAL(KIND=WP), 0 <= TOL < 1
        The tolerance for truncating small singular values.
        See the description of NRNK.

    K

        K (output) INTEGER,  0 <= K <= N
        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.

    REIG

        REIG (output) REAL(KIND=WP) N-by-1 array
        The leading K (K<=N) entries of REIG contain
        the real parts of the computed eigenvalues
        REIG(1:K) + sqrt(-1)*IMEIG(1:K).
        See the descriptions of K, IMEIG, and Z.

    IMEIG

        IMEIG (output) REAL(KIND=WP) N-by-1 array
        The leading K (K<=N) entries of IMEIG contain
        the imaginary parts of the computed eigenvalues
        REIG(1:K) + sqrt(-1)*IMEIG(1:K).
        The eigenvalues are determined as follows:
        If IMEIG(i) == 0, then the corresponding eigenvalue is
        real, LAMBDA(i) = REIG(i).
        If IMEIG(i)>0, then the corresponding complex
        conjugate pair of eigenvalues reads
        LAMBDA(i)   = REIG(i) + sqrt(-1)*IMAG(i)
        LAMBDA(i+1) = REIG(i) - sqrt(-1)*IMAG(i)
        That is, complex conjugate pairs have consecutive
        indices (i,i+1), with the positive imaginary part
        listed first.
        See the descriptions of K, REIG, and Z.

    Z

        Z (workspace/output) REAL(KIND=WP)  M-by-N array
        If JOBZ =='V' then
           Z contains real Ritz vectors as follows:
           If IMEIG(i)=0, then Z(:,i) is an eigenvector of
           the i-th Ritz value; ||Z(:,i)||_2=1.
           If IMEIG(i) > 0 (and IMEIG(i+1) < 0) then
           [Z(:,i) Z(:,i+1)] span an invariant subspace and
           the Ritz values extracted from this subspace are
           REIG(i) + sqrt(-1)*IMEIG(i) and
           REIG(i) - sqrt(-1)*IMEIG(i).
           The corresponding eigenvectors are
           Z(:,i) + sqrt(-1)*Z(:,i+1) and
           Z(:,i) - sqrt(-1)*Z(:,i+1), respectively.
           || Z(:,i:i+1)||_F = 1.
        If JOBZ == 'F', then the above descriptions hold for
        the columns of X(:,1:K)*W(1:K,1:K), where the columns
        of W(1:k,1:K) are the computed eigenvectors of the
        K-by-K Rayleigh quotient. The columns of W(1:K,1:K)
        are similarly structured: If IMEIG(i) == 0 then
        X(:,1:K)*W(:,i) is an eigenvector, and if IMEIG(i)>0
        then X(:,1:K)*W(:,i)+sqrt(-1)*X(:,1:K)*W(:,i+1) and
             X(:,1:K)*W(:,i)-sqrt(-1)*X(:,1:K)*W(:,i+1)
        are the eigenvectors of LAMBDA(i), LAMBDA(i+1).
        See the descriptions of REIG, IMEIG, X and W.

    LDZ

        LDZ (input) INTEGER , LDZ >= M
        The leading dimension of the array Z.

    RES

        RES (output) REAL(KIND=WP) N-by-1 array
        RES(1:K) contains the residuals for the K computed
        Ritz pairs.
        If LAMBDA(i) is real, then
           RES(i) = || A * Z(:,i) - LAMBDA(i)*Z(:,i))||_2.
        If [LAMBDA(i), LAMBDA(i+1)] is a complex conjugate pair
        then
        RES(i)=RES(i+1) = || A * Z(:,i:i+1) - Z(:,i:i+1) *B||_F
        where B = [ real(LAMBDA(i)) imag(LAMBDA(i)) ]
                  [-imag(LAMBDA(i)) real(LAMBDA(i)) ].
        It holds that
        RES(i)   = || A*ZC(:,i)   - LAMBDA(i)  *ZC(:,i)   ||_2
        RES(i+1) = || A*ZC(:,i+1) - LAMBDA(i+1)*ZC(:,i+1) ||_2
        where ZC(:,i)   =  Z(:,i) + sqrt(-1)*Z(:,i+1)
              ZC(:,i+1) =  Z(:,i) - sqrt(-1)*Z(:,i+1)
        See the description of REIG, IMEIG and Z.

    B

        B (output) REAL(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

        LDB (input) INTEGER, LDB >= M
        The leading dimension of the array B.

    W

        W (workspace/output) REAL(KIND=WP) N-by-N array
        On exit, W(1:K,1:K) contains the K computed
        eigenvectors of the matrix Rayleigh quotient (real and
        imaginary parts for each complex conjugate pair of the
        eigenvalues). 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
        left singular vectors of X.

    LDW

        LDW (input) INTEGER, LDW >= N
        The leading dimension of the array W.

    S

        S (workspace/output) REAL(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 SGEEV.
        See the description of K.

    LDS

        LDS (input) INTEGER, LDS >= N
        The leading dimension of the array S.

    WORK

        WORK (workspace/output) REAL(KIND=WP) LWORK-by-1 array
        On exit, WORK(1:N) contains the singular values of
        X (for JOBS=='N') or column scaled X (JOBS=='S', 'C').
        If WHTSVD==4, then WORK(N+1) and WORK(N+2) contain
        scaling factor WORK(N+2)/WORK(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 SGEDMD 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

        LWORK (input) INTEGER
        The minimal length of the workspace vector WORK.
        LWORK is calculated as follows:
        If WHTSVD == 1 ::
           If JOBZ == 'V', then
           LWORK >= MAX(2, N + LWORK_SVD, N+MAX(1,4*N)).
           If JOBZ == 'N'  then
           LWORK >= MAX(2, N + LWORK_SVD, N+MAX(1,3*N)).
           Here LWORK_SVD = MAX(1,3*N+M,5*N) is the minimal
           workspace length of SGESVD.
        If WHTSVD == 2 ::
           If JOBZ == 'V', then
           LWORK >= MAX(2, N + LWORK_SVD, N+MAX(1,4*N))
           If JOBZ == 'N', then
           LWORK >= MAX(2, N + LWORK_SVD, N+MAX(1,3*N))
           Here LWORK_SVD = MAX(M, 5*N*N+4*N)+3*N*N is the
           minimal workspace length of SGESDD.
        If WHTSVD == 3 ::
           If JOBZ == 'V', then
           LWORK >= MAX(2, N+LWORK_SVD,N+MAX(1,4*N))
           If JOBZ == 'N', then
           LWORK >= MAX(2, N+LWORK_SVD,N+MAX(1,3*N))
           Here LWORK_SVD = N+M+MAX(3*N+1,
                           MAX(1,3*N+M,5*N),MAX(1,N))
           is the minimal workspace length of SGESVDQ.
        If WHTSVD == 4 ::
           If JOBZ == 'V', then
           LWORK >= MAX(2, N+LWORK_SVD,N+MAX(1,4*N))
           If JOBZ == 'N', then
           LWORK >= MAX(2, N+LWORK_SVD,N+MAX(1,3*N))
           Here LWORK_SVD = MAX(7,2*M+N,6*N+2*N*N) is the
           minimal workspace length of SGEJSV.
        The above expressions are not simplified in order to
        make the usage of WORK more transparent, and for
        easier checking. In any case, LWORK >= 2.
        If on entry LWORK = -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.

    IWORK

        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

        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 both WORK and
        IWORK. See the descriptions of WORK and IWORK.

    INFO

        INFO (output) INTEGER
        -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.

Author
    Zlatko Drmac

Definition at line 530 of file sgedmd.f90.

Author

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