IB01MD - SLICOT Library Routine Documentation

## IB01MD

### Upper triangular factor in the QR factorization of the block-Hankel matrix

[Specification] [Arguments] [Method] [References] [Comments] [Example]

Purpose

      To construct an upper triangular factor  R  of the concatenated
      block Hankel matrices using input-output data.  The input-output
      data can, optionally, be processed sequentially.

Specification

          SUBROUTINE IB01MD( METH, ALG, BATCH, CONCT, NOBR, M, L, NSMP, U,
         $                   LDU, Y, LDY, R, LDR, IWORK, DWORK, LDWORK,
         $                   IWARN, INFO )
    C     .. Scalar Arguments ..
          INTEGER            INFO, IWARN, L, LDR, LDU, LDWORK, LDY, M, NOBR,
         $                   NSMP
          CHARACTER          ALG, BATCH, CONCT, METH
    C     .. Array Arguments ..
          INTEGER            IWORK(*)
          DOUBLE PRECISION   DWORK(*), R(LDR, *), U(LDU, *), Y(LDY, *)

Arguments

Mode Parameters

      METH    CHARACTER*1
              Specifies the subspace identification method to be used,
              as follows:
              = 'M':  MOESP  algorithm with past inputs and outputs;
              = 'N':  N4SID  algorithm.

      ALG     CHARACTER*1
              Specifies the algorithm for computing the triangular
              factor R, as follows:
              = 'C':  Cholesky algorithm applied to the correlation
                      matrix of the input-output data;
              = 'F':  Fast QR algorithm;
              = 'Q':  QR algorithm applied to the concatenated block
                      Hankel matrices.

      BATCH   CHARACTER*1
              Specifies whether or not sequential data processing is to
              be used, and, for sequential processing, whether or not
              the current data block is the first block, an intermediate
              block, or the last block, as follows:
              = 'F':  the first block in sequential data processing;
              = 'I':  an intermediate block in sequential data
                      processing;
              = 'L':  the last block in sequential data processing;
              = 'O':  one block only (non-sequential data processing).
              NOTE that when  100  cycles of sequential data processing
                   are completed for  BATCH = 'I',  a warning is
                   issued, to prevent for an infinite loop.

      CONCT   CHARACTER*1
              Specifies whether or not the successive data blocks in
              sequential data processing belong to a single experiment,
              as follows:
              = 'C':  the current data block is a continuation of the
                      previous data block and/or it will be continued
                      by the next data block;
              = 'N':  there is no connection between the current data
                      block and the previous and/or the next ones.
              This parameter is not used if BATCH = 'O'.

Input/Output Parameters

      NOBR    (input) INTEGER
              The number of block rows,  s,  in the input and output
              block Hankel matrices to be processed.  NOBR > 0.
              (In the MOESP theory,  NOBR  should be larger than  n,
              the estimated dimension of state vector.)

      M       (input) INTEGER
              The number of system inputs.  M >= 0.
              When M = 0, no system inputs are processed.

      L       (input) INTEGER
              The number of system outputs.  L > 0.

      NSMP    (input) INTEGER
              The number of rows of matrices  U  and  Y  (number of
              samples,  t). (When sequential data processing is used,
              NSMP  is the number of samples of the current data
              block.)
              NSMP >= 2*(M+L+1)*NOBR - 1,  for non-sequential
                                           processing;
              NSMP >= 2*NOBR,  for sequential processing.
              The total number of samples when calling the routine with
              BATCH = 'L'  should be at least  2*(M+L+1)*NOBR - 1.
              The  NSMP  argument may vary from a cycle to another in
              sequential data processing, but  NOBR, M,  and  L  should
              be kept constant. For efficiency, it is advisable to use
              NSMP  as large as possible.

      U       (input) DOUBLE PRECISION array, dimension (LDU,M)
              The leading NSMP-by-M part of this array must contain the
              t-by-m input-data sequence matrix  U,
              U = [u_1 u_2 ... u_m].  Column  j  of  U  contains the
              NSMP  values of the j-th input component for consecutive
              time increments.
              If M = 0, this array is not referenced.

      LDU     INTEGER
              The leading dimension of the array U.
              LDU >= NSMP, if M > 0;
              LDU >= 1,    if M = 0.

      Y       (input) DOUBLE PRECISION array, dimension (LDY,L)
              The leading NSMP-by-L part of this array must contain the
              t-by-l output-data sequence matrix  Y,
              Y = [y_1 y_2 ... y_l].  Column  j  of  Y  contains the
              NSMP  values of the j-th output component for consecutive
              time increments.

      LDY     INTEGER
              The leading dimension of the array Y.  LDY >= NSMP.

      R       (output or input/output) DOUBLE PRECISION array, dimension
              ( LDR,2*(M+L)*NOBR )
              On exit, if INFO = 0 and ALG = 'Q', or (ALG = 'C' or 'F',
              and BATCH = 'L' or 'O'), the leading
              2*(M+L)*NOBR-by-2*(M+L)*NOBR upper triangular part of
              this array contains the (current) upper triangular factor
              R from the QR factorization of the concatenated block
              Hankel matrices. The diagonal elements of R are positive
              when the Cholesky algorithm was successfully used.
              On exit, if ALG = 'C' and BATCH = 'F' or 'I', the leading
              2*(M+L)*NOBR-by-2*(M+L)*NOBR upper triangular part of this
              array contains the current upper triangular part of the
              correlation matrix in sequential data processing.
              If ALG = 'F' and BATCH = 'F' or 'I', the array R is not
              referenced.
              On entry, if ALG = 'C', or ALG = 'Q', and BATCH = 'I' or
              'L', the leading  2*(M+L)*NOBR-by-2*(M+L)*NOBR  upper
              triangular part of this array must contain the upper
              triangular matrix R computed at the previous call of this
              routine in sequential data processing. The array R need
              not be set on entry if ALG = 'F' or if BATCH = 'F' or 'O'.

      LDR     INTEGER
              The leading dimension of the array  R.
              LDR >= 2*(M+L)*NOBR.

Workspace

      IWORK   INTEGER array, dimension (LIWORK)
              LIWORK >= M+L, if ALG = 'F';
              LIWORK >= 0,   if ALG = 'C' or 'Q'.

      DWORK   DOUBLE PRECISION array, dimension (LDWORK)
              On exit, if  INFO = 0,  DWORK(1)  returns the optimal
              value of LDWORK.
              On exit, if  INFO = -17,  DWORK(1)  returns the minimum
              value of LDWORK.
              Let
              k = 0,               if CONCT = 'N' and ALG = 'C' or 'Q';
              k = 2*NOBR-1,        if CONCT = 'C' and ALG = 'C' or 'Q';
              k = 2*NOBR*(M+L+1),  if CONCT = 'N' and ALG = 'F';
              k = 2*NOBR*(M+L+2),  if CONCT = 'C' and ALG = 'F'.
              The first (M+L)*k elements of  DWORK  should be preserved
              during successive calls of the routine with  BATCH = 'F'
              or  'I',  till the final call with  BATCH = 'L'.

      LDWORK  INTEGER
              The length of the array DWORK.
              LDWORK >= (4*NOBR-2)*(M+L), if ALG = 'C', BATCH <> 'O' and
                                      CONCT = 'C';
              LDWORK >= 1,            if ALG = 'C', BATCH = 'O' or
                                      CONCT = 'N';
              LDWORK >= (M+L)*2*NOBR*(M+L+3), if ALG = 'F',
                                      BATCH <> 'O' and CONCT = 'C';
              LDWORK >= (M+L)*2*NOBR*(M+L+1), if ALG = 'F',
                                      BATCH = 'F', 'I' and CONCT = 'N';
              LDWORK >= (M+L)*4*NOBR*(M+L+1)+(M+L)*2*NOBR, if ALG = 'F',
                                      BATCH = 'L' and CONCT = 'N', or
                                      BATCH = 'O';
              LDWORK >= 4*(M+L)*NOBR, if ALG = 'Q', BATCH = 'F' or 'O',
                                      and LDR >= NS = NSMP - 2*NOBR + 1;
              LDWORK >= 6*(M+L)*NOBR, if ALG = 'Q', BATCH = 'F' or 'O',
                                      and LDR < NS, or BATCH = 'I' or
                                      'L' and CONCT = 'N';
              LDWORK >= 4*(NOBR+1)*(M+L)*NOBR, if ALG = 'Q', BATCH = 'I'
                                      or 'L' and CONCT = 'C'.
              The workspace used for ALG = 'Q' is
                        LDRWRK*2*(M+L)*NOBR + 4*(M+L)*NOBR,
              where LDRWRK = LDWORK/(2*(M+L)*NOBR) - 2; recommended
              value LDRWRK = NS, assuming a large enough cache size.
              For good performance,  LDWORK  should be larger.

Warning Indicator

      IWARN   INTEGER
              = 0:  no warning;
              = 1:  the number of 100 cycles in sequential data
                    processing has been exhausted without signaling
                    that the last block of data was get; the cycle
                    counter was reinitialized;
              = 2:  a fast algorithm was requested (ALG = 'C' or 'F'),
                    but it failed, and the QR algorithm was then used
                    (non-sequential data processing).

Error Indicator

      INFO    INTEGER
              = 0:  successful exit;
              < 0:  if INFO = -i, the i-th argument had an illegal
                    value;
              = 1:  a fast algorithm was requested (ALG = 'C', or 'F')
                    in sequential data processing, but it failed. The
                    routine can be repeatedly called again using the
                    standard QR algorithm.

Method

      1) For non-sequential data processing using QR algorithm, a
      t x 2(m+l)s  matrix H is constructed, where

           H = [ Uf'         Up'      Y'      ],  for METH = 'M',
                   s+1,2s,t    1,s,t   1,2s,t

           H = [ U'       Y'      ],              for METH = 'N',
                  1,2s,t   1,2s,t

      and  Up     , Uf        , U      , and  Y        are block Hankel
             1,s,t    s+1,2s,t   1,2s,t        1,2s,t
      matrices defined in terms of the input and output data [3].
      A QR factorization is used to compress the data.
      The fast QR algorithm uses a QR factorization which exploits
      the block-Hankel structure. Actually, the Cholesky factor of H'*H
      is computed.

      2) For sequential data processing using QR algorithm, the QR
      decomposition is done sequentially, by updating the upper
      triangular factor  R.  This is also performed internally if the
      workspace is not large enough to accommodate an entire batch.

      3) For non-sequential or sequential data processing using
      Cholesky algorithm, the correlation matrix of input-output data is
      computed (sequentially, if requested), taking advantage of the
      block Hankel structure [7].  Then, the Cholesky factor of the
      correlation matrix is found, if possible.

References

      [1] Verhaegen M., and Dewilde, P.
          Subspace Model Identification. Part 1: The output-error
          state-space model identification class of algorithms.
          Int. J. Control, 56, pp. 1187-1210, 1992.

      [2] Verhaegen M.
          Subspace Model Identification. Part 3: Analysis of the
          ordinary output-error state-space model identification
          algorithm.
          Int. J. Control, 58, pp. 555-586, 1993.

      [3] Verhaegen M.
          Identification of the deterministic part of MIMO state space
          models given in innovations form from input-output data.
          Automatica, Vol.30, No.1, pp.61-74, 1994.

      [4] Van Overschee, P., and De Moor, B.
          N4SID: Subspace Algorithms for the Identification of
          Combined Deterministic-Stochastic Systems.
          Automatica, Vol.30, No.1, pp. 75-93, 1994.

      [5] Peternell, K., Scherrer, W. and Deistler, M.
          Statistical Analysis of Novel Subspace Identification Methods.
          Signal Processing, 52, pp. 161-177, 1996.

      [6] Sima, V.
          Subspace-based Algorithms for Multivariable System
          Identification.
          Studies in Informatics and Control, 5, pp. 335-344, 1996.

      [7] Sima, V.
          Cholesky or QR Factorization for Data Compression in
          Subspace-based Identification ?
          Proceedings of the Second NICONET Workshop on ``Numerical
          Control Software: SLICOT, a Useful Tool in Industry'',
          December 3, 1999, INRIA Rocquencourt, France, pp. 75-80, 1999.

Numerical Aspects

      The implemented method is numerically stable (when QR algorithm is
      used), reliable and efficient. The fast Cholesky or QR algorithms
      are more efficient, but the accuracy could diminish by forming the
      correlation matrix.
                                         2
      The QR algorithm needs 0(t(2(m+l)s) ) floating point operations.
                                            2              3
      The Cholesky algorithm needs 0(2t(m+l) s)+0((2(m+l)s) ) floating
      point operations.
                                           2           3 2
      The fast QR algorithm needs 0(2t(m+l) s)+0(4(m+l) s ) floating
      point operations.

Further Comments

      For ALG = 'Q', BATCH = 'O' and LDR < NS, or BATCH <> 'O', the
      calculations could be rather inefficient if only minimal workspace
      (see argument LDWORK) is provided. It is advisable to provide as
      much workspace as possible. Almost optimal efficiency can be
      obtained for  LDWORK = (NS+2)*(2*(M+L)*NOBR),  assuming that the
      cache size is large enough to accommodate R, U, Y, and DWORK.

Example

Program Text

      None

Program Data

      None

Program Results

      None

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