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SUBROUTINE AB09JD( JOBV, JOBW, JOBINV, DICO, EQUIL, ORDSEL,
$ N, NV, NW, M, P, NR, ALPHA, A, LDA, B, LDB,
$ C, LDC, D, LDD, AV, LDAV, BV, LDBV,
$ CV, LDCV, DV, LDDV, AW, LDAW, BW, LDBW,
$ CW, LDCW, DW, LDDW, NS, HSV, TOL1, TOL2,
$ IWORK, DWORK, LDWORK, IWARN, INFO )
C
C PURPOSE
C
C To compute a reduced order model (Ar,Br,Cr,Dr) for an original
C state-space representation (A,B,C,D) by using the frequency
C weighted optimal Hankel-norm approximation method.
C The Hankel norm of the weighted error
C
C op(V)*(G-Gr)*op(W)
C
C is minimized, where G and Gr are the transfer-function matrices
C of the original and reduced systems, respectively, V and W are
C invertible transfer-function matrices representing the left and
C right frequency weights, and op(X) denotes X, inv(X), conj(X) or
C conj(inv(X)). V and W are specified by their state space
C realizations (AV,BV,CV,DV) and (AW,BW,CW,DW), respectively.
C When minimizing ||V*(G-Gr)*W||, V and W must be antistable.
C When minimizing inv(V)*(G-Gr)*inv(W), V and W must have only
C antistable zeros.
C When minimizing conj(V)*(G-Gr)*conj(W), V and W must be stable.
C When minimizing conj(inv(V))*(G-Gr)*conj(inv(W)), V and W must
C be minimum-phase.
C If the original system is unstable, then the frequency weighted
C Hankel-norm approximation is computed only for the
C ALPHA-stable part of the system.
C
C For a transfer-function matrix G, conj(G) denotes the conjugate
C of G given by G'(-s) for a continuous-time system or G'(1/z)
C for a discrete-time system.
C
C ARGUMENTS
C
C Mode Parameters
C
C JOBV CHARACTER*1
C Specifies the left frequency-weighting as follows:
C = 'N': V = I;
C = 'V': op(V) = V;
C = 'I': op(V) = inv(V);
C = 'C': op(V) = conj(V);
C = 'R': op(V) = conj(inv(V)).
C
C JOBW CHARACTER*1
C Specifies the right frequency-weighting as follows:
C = 'N': W = I;
C = 'W': op(W) = W;
C = 'I': op(W) = inv(W);
C = 'C': op(W) = conj(W);
C = 'R': op(W) = conj(inv(W)).
C
C JOBINV CHARACTER*1
C Specifies the computational approach to be used as
C follows:
C = 'N': use the inverse free descriptor system approach;
C = 'I': use the inversion based standard approach;
C = 'A': switch automatically to the inverse free
C descriptor approach in case of badly conditioned
C feedthrough matrices in V or W (see METHOD).
C
C DICO CHARACTER*1
C Specifies the type of the original system as follows:
C = 'C': continuous-time system;
C = 'D': discrete-time system.
C
C EQUIL CHARACTER*1
C Specifies whether the user wishes to preliminarily
C equilibrate the triplet (A,B,C) as follows:
C = 'S': perform equilibration (scaling);
C = 'N': do not perform equilibration.
C
C ORDSEL CHARACTER*1
C Specifies the order selection method as follows:
C = 'F': the resulting order NR is fixed;
C = 'A': the resulting order NR is automatically determined
C on basis of the given tolerance TOL1.
C
C Input/Output Parameters
C
C N (input) INTEGER
C The order of the original state-space representation,
C i.e., the order of the matrix A. N >= 0.
C
C NV (input) INTEGER
C The order of the realization of the left frequency
C weighting V, i.e., the order of the matrix AV. NV >= 0.
C
C NW (input) INTEGER
C The order of the realization of the right frequency
C weighting W, i.e., the order of the matrix AW. NW >= 0.
C
C M (input) INTEGER
C The number of system inputs. M >= 0.
C
C P (input) INTEGER
C The number of system outputs. P >= 0.
C
C NR (input/output) INTEGER
C On entry with ORDSEL = 'F', NR is the desired order of
C the resulting reduced order system. 0 <= NR <= N.
C On exit, if INFO = 0, NR is the order of the resulting
C reduced order model. For a system with NU ALPHA-unstable
C eigenvalues and NS ALPHA-stable eigenvalues (NU+NS = N),
C NR is set as follows: if ORDSEL = 'F', NR is equal to
C NU+MIN(MAX(0,NR-NU-KR+1),NMIN), where KR is the
C multiplicity of the Hankel singular value HSV(NR-NU+1),
C NR is the desired order on entry, and NMIN is the order
C of a minimal realization of the ALPHA-stable part of the
C given system; NMIN is determined as the number of Hankel
C singular values greater than NS*EPS*HNORM(As,Bs,Cs), where
C EPS is the machine precision (see LAPACK Library Routine
C DLAMCH) and HNORM(As,Bs,Cs) is the Hankel norm of the
C ALPHA-stable part of the weighted system (computed in
C HSV(1));
C if ORDSEL = 'A', NR is the sum of NU and the number of
C Hankel singular values greater than
C MAX(TOL1,NS*EPS*HNORM(As,Bs,Cs)).
C
C ALPHA (input) DOUBLE PRECISION
C Specifies the ALPHA-stability boundary for the eigenvalues
C of the state dynamics matrix A. For a continuous-time
C system (DICO = 'C'), ALPHA <= 0 is the boundary value for
C the real parts of eigenvalues, while for a discrete-time
C system (DICO = 'D'), 0 <= ALPHA <= 1 represents the
C boundary value for the moduli of eigenvalues.
C The ALPHA-stability domain does not include the boundary.
C
C A (input/output) DOUBLE PRECISION array, dimension (LDA,N)
C On entry, the leading N-by-N part of this array must
C contain the state dynamics matrix A.
C On exit, if INFO = 0, the leading NR-by-NR part of this
C array contains the state dynamics matrix Ar of the
C reduced order system in a real Schur form.
C The resulting A has a block-diagonal form with two blocks.
C For a system with NU ALPHA-unstable eigenvalues and
C NS ALPHA-stable eigenvalues (NU+NS = N), the leading
C NU-by-NU block contains the unreduced part of A
C corresponding to ALPHA-unstable eigenvalues.
C The trailing (NR+NS-N)-by-(NR+NS-N) block contains
C the reduced part of A corresponding to ALPHA-stable
C eigenvalues.
C
C LDA INTEGER
C The leading dimension of array A. LDA >= MAX(1,N).
C
C B (input/output) DOUBLE PRECISION array, dimension (LDB,M)
C On entry, the leading N-by-M part of this array must
C contain the original input/state matrix B.
C On exit, if INFO = 0, the leading NR-by-M part of this
C array contains the input/state matrix Br of the reduced
C order system.
C
C LDB INTEGER
C The leading dimension of array B. LDB >= MAX(1,N).
C
C C (input/output) DOUBLE PRECISION array, dimension (LDC,N)
C On entry, the leading P-by-N part of this array must
C contain the original state/output matrix C.
C On exit, if INFO = 0, the leading P-by-NR part of this
C array contains the state/output matrix Cr of the reduced
C order system.
C
C LDC INTEGER
C The leading dimension of array C. LDC >= MAX(1,P).
C
C D (input/output) DOUBLE PRECISION array, dimension (LDD,M)
C On entry, the leading P-by-M part of this array must
C contain the original input/output matrix D.
C On exit, if INFO = 0, the leading P-by-M part of this
C array contains the input/output matrix Dr of the reduced
C order system.
C
C LDD INTEGER
C The leading dimension of array D. LDD >= MAX(1,P).
C
C AV (input/output) DOUBLE PRECISION array, dimension (LDAV,NV)
C On entry, if JOBV <> 'N', the leading NV-by-NV part of
C this array must contain the state matrix AV of a state
C space realization of the left frequency weighting V.
C On exit, if JOBV <> 'N', and INFO = 0, the leading
C NV-by-NV part of this array contains the real Schur form
C of AV.
C AV is not referenced if JOBV = 'N'.
C
C LDAV INTEGER
C The leading dimension of the array AV.
C LDAV >= MAX(1,NV), if JOBV <> 'N';
C LDAV >= 1, if JOBV = 'N'.
C
C BV (input/output) DOUBLE PRECISION array, dimension (LDBV,P)
C On entry, if JOBV <> 'N', the leading NV-by-P part of
C this array must contain the input matrix BV of a state
C space realization of the left frequency weighting V.
C On exit, if JOBV <> 'N', and INFO = 0, the leading
C NV-by-P part of this array contains the transformed
C input matrix BV corresponding to the transformed AV.
C BV is not referenced if JOBV = 'N'.
C
C LDBV INTEGER
C The leading dimension of the array BV.
C LDBV >= MAX(1,NV), if JOBV <> 'N';
C LDBV >= 1, if JOBV = 'N'.
C
C CV (input/output) DOUBLE PRECISION array, dimension (LDCV,NV)
C On entry, if JOBV <> 'N', the leading P-by-NV part of
C this array must contain the output matrix CV of a state
C space realization of the left frequency weighting V.
C On exit, if JOBV <> 'N', and INFO = 0, the leading
C P-by-NV part of this array contains the transformed output
C matrix CV corresponding to the transformed AV.
C CV is not referenced if JOBV = 'N'.
C
C LDCV INTEGER
C The leading dimension of the array CV.
C LDCV >= MAX(1,P), if JOBV <> 'N';
C LDCV >= 1, if JOBV = 'N'.
C
C DV (input) DOUBLE PRECISION array, dimension (LDDV,P)
C If JOBV <> 'N', the leading P-by-P part of this array
C must contain the feedthrough matrix DV of a state space
C realization of the left frequency weighting V.
C DV is not referenced if JOBV = 'N'.
C
C LDDV INTEGER
C The leading dimension of the array DV.
C LDDV >= MAX(1,P), if JOBV <> 'N';
C LDDV >= 1, if JOBV = 'N'.
C
C AW (input/output) DOUBLE PRECISION array, dimension (LDAW,NW)
C On entry, if JOBW <> 'N', the leading NW-by-NW part of
C this array must contain the state matrix AW of a state
C space realization of the right frequency weighting W.
C On exit, if JOBW <> 'N', and INFO = 0, the leading
C NW-by-NW part of this array contains the real Schur form
C of AW.
C AW is not referenced if JOBW = 'N'.
C
C LDAW INTEGER
C The leading dimension of the array AW.
C LDAW >= MAX(1,NW), if JOBW <> 'N';
C LDAW >= 1, if JOBW = 'N'.
C
C BW (input/output) DOUBLE PRECISION array, dimension (LDBW,M)
C On entry, if JOBW <> 'N', the leading NW-by-M part of
C this array must contain the input matrix BW of a state
C space realization of the right frequency weighting W.
C On exit, if JOBW <> 'N', and INFO = 0, the leading
C NW-by-M part of this array contains the transformed
C input matrix BW corresponding to the transformed AW.
C BW is not referenced if JOBW = 'N'.
C
C LDBW INTEGER
C The leading dimension of the array BW.
C LDBW >= MAX(1,NW), if JOBW <> 'N';
C LDBW >= 1, if JOBW = 'N'.
C
C CW (input/output) DOUBLE PRECISION array, dimension (LDCW,NW)
C On entry, if JOBW <> 'N', the leading M-by-NW part of
C this array must contain the output matrix CW of a state
C space realization of the right frequency weighting W.
C On exit, if JOBW <> 'N', and INFO = 0, the leading
C M-by-NW part of this array contains the transformed output
C matrix CW corresponding to the transformed AW.
C CW is not referenced if JOBW = 'N'.
C
C LDCW INTEGER
C The leading dimension of the array CW.
C LDCW >= MAX(1,M), if JOBW <> 'N';
C LDCW >= 1, if JOBW = 'N'.
C
C DW (input) DOUBLE PRECISION array, dimension (LDDW,M)
C If JOBW <> 'N', the leading M-by-M part of this array
C must contain the feedthrough matrix DW of a state space
C realization of the right frequency weighting W.
C DW is not referenced if JOBW = 'N'.
C
C LDDW INTEGER
C The leading dimension of the array DW.
C LDDW >= MAX(1,M), if JOBW <> 'N';
C LDDW >= 1, if JOBW = 'N'.
C
C NS (output) INTEGER
C The dimension of the ALPHA-stable subsystem.
C
C HSV (output) DOUBLE PRECISION array, dimension (N)
C If INFO = 0, the leading NS elements of this array contain
C the Hankel singular values, ordered decreasingly, of the
C projection G1s of op(V)*G1*op(W) (see METHOD), where G1
C is the ALPHA-stable part of the original system.
C
C Tolerances
C
C TOL1 DOUBLE PRECISION
C If ORDSEL = 'A', TOL1 contains the tolerance for
C determining the order of reduced system.
C For model reduction, the recommended value is
C TOL1 = c*HNORM(G1s), where c is a constant in the
C interval [0.00001,0.001], and HNORM(G1s) is the
C Hankel-norm of the projection G1s of op(V)*G1*op(W)
C (see METHOD), computed in HSV(1).
C If TOL1 <= 0 on entry, the used default value is
C TOL1 = NS*EPS*HNORM(G1s), where NS is the number of
C ALPHA-stable eigenvalues of A and EPS is the machine
C precision (see LAPACK Library Routine DLAMCH).
C If ORDSEL = 'F', the value of TOL1 is ignored.
C TOL1 < 1.
C
C TOL2 DOUBLE PRECISION
C The tolerance for determining the order of a minimal
C realization of the ALPHA-stable part of the given system.
C The recommended value is TOL2 = NS*EPS*HNORM(G1s).
C This value is used by default if TOL2 <= 0 on entry.
C If TOL2 > 0 and ORDSEL = 'A', then TOL2 <= TOL1.
C TOL2 < 1.
C
C Workspace
C
C IWORK INTEGER array, dimension (LIWORK)
C LIWORK = MAX(1,M,c,d), if DICO = 'C',
C LIWORK = MAX(1,N,M,c,d), if DICO = 'D', where
C c = 0, if JOBV = 'N',
C c = MAX(2*P,NV+P+N+6,2*NV+P+2), if JOBV <> 'N',
C d = 0, if JOBW = 'N',
C d = MAX(2*M,NW+M+N+6,2*NW+M+2), if JOBW <> 'N'.
C
C DWORK DOUBLE PRECISION array, dimension (LDWORK)
C On exit, if INFO = 0, DWORK(1) returns the optimal value
C of LDWORK.
C
C LDWORK INTEGER
C The length of the array DWORK.
C LDWORK >= MAX( LDW1, LDW2, LDW3, LDW4 ), where
C for NVP = NV+P and NWM = NW+M we have
C LDW1 = 0 if JOBV = 'N' and
C LDW1 = 2*NVP*(NVP+P) + P*P +
C MAX( 2*NVP*NVP + MAX( 11*NVP+16, P*NVP ),
C NVP*N + MAX( NVP*N+N*N, P*N, P*M ) )
C if JOBV <> 'N',
C LDW2 = 0 if JOBW = 'N' and
C LDW2 = 2*NWM*(NWM+M) + M*M +
C MAX( 2*NWM*NWM + MAX( 11*NWM+16, M*NWM ),
C NWM*N + MAX( NWM*N+N*N, M*N, P*M ) )
C if JOBW <> 'N',
C LDW3 = N*(2*N + MAX(N,M,P) + 5) + N*(N+1)/2,
C LDW4 = N*(M+P+2) + 2*M*P + MIN(N,M) +
C MAX( 3*M+1, MIN(N,M)+P ).
C For optimum performance LDWORK should be larger.
C
C Warning Indicator
C
C IWARN INTEGER
C = 0: no warning;
C = 1: with ORDSEL = 'F', the selected order NR is greater
C than NSMIN, the sum of the order of the
C ALPHA-unstable part and the order of a minimal
C realization of the ALPHA-stable part of the given
C system. In this case, the resulting NR is set equal
C to NSMIN.
C = 2: with ORDSEL = 'F', the selected order NR is less
C than the order of the ALPHA-unstable part of the
C given system. In this case NR is set equal to the
C order of the ALPHA-unstable part.
C
C Error Indicator
C
C INFO INTEGER
C = 0: successful exit;
C < 0: if INFO = -i, the i-th argument had an illegal
C value;
C = 1: the computation of the ordered real Schur form of A
C failed;
C = 2: the separation of the ALPHA-stable/unstable
C diagonal blocks failed because of very close
C eigenvalues;
C = 3: the reduction of AV to a real Schur form failed;
C = 4: the reduction of AW to a real Schur form failed;
C = 5: the reduction to generalized Schur form of the
C descriptor pair corresponding to the inverse of V
C failed;
C = 6: the reduction to generalized Schur form of the
C descriptor pair corresponding to the inverse of W
C failed;
C = 7: the computation of Hankel singular values failed;
C = 8: the computation of stable projection in the
C Hankel-norm approximation algorithm failed;
C = 9: the order of computed stable projection in the
C Hankel-norm approximation algorithm differs
C from the order of Hankel-norm approximation;
C = 10: the reduction of AV-BV*inv(DV)*CV to a
C real Schur form failed;
C = 11: the reduction of AW-BW*inv(DW)*CW to a
C real Schur form failed;
C = 12: the solution of the Sylvester equation failed
C because the poles of V (if JOBV = 'V') or of
C conj(V) (if JOBV = 'C') are not distinct from
C the poles of G1 (see METHOD);
C = 13: the solution of the Sylvester equation failed
C because the poles of W (if JOBW = 'W') or of
C conj(W) (if JOBW = 'C') are not distinct from
C the poles of G1 (see METHOD);
C = 14: the solution of the Sylvester equation failed
C because the zeros of V (if JOBV = 'I') or of
C conj(V) (if JOBV = 'R') are not distinct from
C the poles of G1sr (see METHOD);
C = 15: the solution of the Sylvester equation failed
C because the zeros of W (if JOBW = 'I') or of
C conj(W) (if JOBW = 'R') are not distinct from
C the poles of G1sr (see METHOD);
C = 16: the solution of the generalized Sylvester system
C failed because the zeros of V (if JOBV = 'I') or
C of conj(V) (if JOBV = 'R') are not distinct from
C the poles of G1sr (see METHOD);
C = 17: the solution of the generalized Sylvester system
C failed because the zeros of W (if JOBW = 'I') or
C of conj(W) (if JOBW = 'R') are not distinct from
C the poles of G1sr (see METHOD);
C = 18: op(V) is not antistable;
C = 19: op(W) is not antistable;
C = 20: V is not invertible;
C = 21: W is not invertible.
C
C METHOD
C
C Let G be the transfer-function matrix of the original
C linear system
C
C d[x(t)] = Ax(t) + Bu(t)
C y(t) = Cx(t) + Du(t), (1)
C
C where d[x(t)] is dx(t)/dt for a continuous-time system and x(t+1)
C for a discrete-time system. The subroutine AB09JD determines
C the matrices of a reduced order system
C
C d[z(t)] = Ar*z(t) + Br*u(t)
C yr(t) = Cr*z(t) + Dr*u(t), (2)
C
C such that the corresponding transfer-function matrix Gr minimizes
C the Hankel-norm of the frequency-weighted error
C
C op(V)*(G-Gr)*op(W). (3)
C
C For minimizing (3) with op(V) = V and op(W) = W, V and W are
C assumed to have poles distinct from those of G, while with
C op(V) = conj(V) and op(W) = conj(W), conj(V) and conj(W) are
C assumed to have poles distinct from those of G. For minimizing (3)
C with op(V) = inv(V) and op(W) = inv(W), V and W are assumed to
C have zeros distinct from the poles of G, while with
C op(V) = conj(inv(V)) and op(W) = conj(inv(W)), conj(V) and conj(W)
C are assumed to have zeros distinct from the poles of G.
C
C Note: conj(G) = G'(-s) for a continuous-time system and
C conj(G) = G'(1/z) for a discrete-time system.
C
C The following procedure is used to reduce G (see [1]):
C
C 1) Decompose additively G as
C
C G = G1 + G2,
C
C such that G1 = (A1,B1,C1,D) has only ALPHA-stable poles and
C G2 = (A2,B2,C2,0) has only ALPHA-unstable poles.
C
C 2) Compute G1s, the projection of op(V)*G1*op(W) containing the
C poles of G1, using explicit formulas [4] or the inverse-free
C descriptor system formulas of [5].
C
C 3) Determine G1sr, the optimal Hankel-norm approximation of G1s,
C of order r.
C
C 4) Compute G1r, the projection of inv(op(V))*G1sr*inv(op(W))
C containing the poles of G1sr, using explicit formulas [4]
C or the inverse-free descriptor system formulas of [5].
C
C 5) Assemble the reduced model Gr as
C
C Gr = G1r + G2.
C
C To reduce the weighted ALPHA-stable part G1s at step 3, the
C optimal Hankel-norm approximation method of [2], based on the
C square-root balancing projection formulas of [3], is employed.
C
C The optimal weighted approximation error satisfies
C
C HNORM[op(V)*(G-Gr)*op(W)] >= S(r+1),
C
C where S(r+1) is the (r+1)-th Hankel singular value of G1s, the
C transfer-function matrix computed at step 2 of the above
C procedure, and HNORM(.) denotes the Hankel-norm.
C
C REFERENCES
C
C [1] Latham, G.A. and Anderson, B.D.O.
C Frequency-weighted optimal Hankel-norm approximation of stable
C transfer functions.
C Systems & Control Letters, Vol. 5, pp. 229-236, 1985.
C
C [2] Glover, K.
C All optimal Hankel norm approximation of linear
C multivariable systems and their L-infinity error bounds.
C Int. J. Control, Vol. 36, pp. 1145-1193, 1984.
C
C [3] Tombs, M.S. and Postlethwaite, I.
C Truncated balanced realization of stable, non-minimal
C state-space systems.
C Int. J. Control, Vol. 46, pp. 1319-1330, 1987.
C
C [4] Varga, A.
C Explicit formulas for an efficient implementation
C of the frequency-weighting model reduction approach.
C Proc. 1993 European Control Conference, Groningen, NL,
C pp. 693-696, 1993.
C
C [5] Varga, A.
C Efficient and numerically reliable implementation of the
C frequency-weighted Hankel-norm approximation model reduction
C approach.
C Proc. 2001 ECC, Porto, Portugal, 2001.
C
C NUMERICAL ASPECTS
C
C The implemented methods rely on an accuracy enhancing square-root
C technique.
C
C CONTRIBUTORS
C
C A. Varga, German Aerospace Center, Oberpfaffenhofen, March 2001.
C D. Sima, University of Bucharest, April 2001.
C V. Sima, Research Institute for Informatics, Bucharest, Apr. 2001.
C
C REVISIONS
C
C A. Varga, German Aerospace Center, Oberpfaffenhofen, May 2001.
C V. Sima, Research Institute for Informatics, Bucharest, June 2001,
C March 2005.
C
C KEYWORDS
C
C Frequency weighting, model reduction, multivariable system,
C state-space model, state-space representation.
C
C ******************************************************************
C
C .. Parameters ..
DOUBLE PRECISION C100, ONE, P0001, ZERO
PARAMETER ( C100 = 100.0D0, ONE = 1.0D0, P0001 = 0.0001D0,
$ ZERO = 0.0D0 )
C .. Scalar Arguments ..
CHARACTER DICO, EQUIL, JOBINV, JOBV, JOBW, ORDSEL
INTEGER INFO, IWARN, LDA, LDAV, LDAW, LDB, LDBV, LDBW,
$ LDC, LDCV, LDCW, LDD, LDDV, LDDW, LDWORK, M, N,
$ NR, NS, NV, NW, P
DOUBLE PRECISION ALPHA, TOL1, TOL2
C .. Array Arguments ..
INTEGER IWORK(*)
DOUBLE PRECISION A(LDA,*), AV(LDAV,*), AW(LDAW,*),
$ B(LDB,*), BV(LDBV,*), BW(LDBW,*),
$ C(LDC,*), CV(LDCV,*), CW(LDCW,*),
$ D(LDD,*), DV(LDDV,*), DW(LDDW,*), DWORK(*),
$ HSV(*)
C .. Local Scalars ..
CHARACTER JOBVL, JOBWL
LOGICAL AUTOM, CONJV, CONJW, DISCR, FIXORD, INVFR,
$ LEFTI, LEFTW, RIGHTI, RIGHTW
INTEGER IERR, IWARNL, KAV, KAW, KBV, KBW, KCV, KCW, KDV,
$ KDW, KEV, KEW, KI, KL, KU, KW, LDABV, LDABW,
$ LDCDV, LDCDW, LW, NRA, NU, NU1, NVP, NWM, RANK
DOUBLE PRECISION ALPWRK, MAXRED, RCOND, SQREPS, TOL, WRKOPT
C .. Local Arrays ..
DOUBLE PRECISION TEMP(1)
C .. External Functions ..
LOGICAL LSAME
DOUBLE PRECISION DLAMCH
EXTERNAL DLAMCH, LSAME
C .. External Subroutines ..
EXTERNAL AB07ND, AB08MD, AB09CX, AB09JV, AB09JW, AG07BD,
$ DLACPY, TB01ID, TB01KD, XERBLA
C .. Intrinsic Functions ..
INTRINSIC DBLE, MAX, MIN, SQRT
C .. Executable Statements ..
C
INFO = 0
IWARN = 0
DISCR = LSAME( DICO, 'D' )
FIXORD = LSAME( ORDSEL, 'F' )
LEFTI = LSAME( JOBV, 'I' ) .OR. LSAME( JOBV, 'R' )
LEFTW = LSAME( JOBV, 'V' ) .OR. LSAME( JOBV, 'C' ) .OR. LEFTI
CONJV = LSAME( JOBV, 'C' ) .OR. LSAME( JOBV, 'R' )
RIGHTI = LSAME( JOBW, 'I' ) .OR. LSAME( JOBW, 'R' )
RIGHTW = LSAME( JOBW, 'W' ) .OR. LSAME( JOBW, 'C' ) .OR. RIGHTI
CONJW = LSAME( JOBW, 'C' ) .OR. LSAME( JOBW, 'R' )
INVFR = LSAME( JOBINV, 'N' )
AUTOM = LSAME( JOBINV, 'A' )
C
LW = 1
IF( LEFTW ) THEN
NVP = NV + P
LW = MAX( LW, 2*NVP*( NVP + P ) + P*P +
$ MAX( 2*NVP*NVP + MAX( 11*NVP + 16, P*NVP ),
$ NVP*N + MAX( NVP*N+N*N, P*N, P*M ) ) )
END IF
IF( RIGHTW ) THEN
NWM = NW + M
LW = MAX( LW, 2*NWM*( NWM + M ) + M*M +
$ MAX( 2*NWM*NWM + MAX( 11*NWM + 16, M*NWM ),
$ NWM*N + MAX( NWM*N+N*N, M*N, P*M ) ) )
END IF
LW = MAX( LW, N*( 2*N + MAX( N, M, P ) + 5 ) + ( N*( N + 1 ) )/2 )
LW = MAX( LW, N*( M + P + 2 ) + 2*M*P + MIN( N, M ) +
$ MAX ( 3*M + 1, MIN( N, M ) + P ) )
C
C Check the input scalar arguments.
C
IF( .NOT. ( LSAME( JOBV, 'N' ) .OR. LEFTW ) ) THEN
INFO = -1
ELSE IF( .NOT. ( LSAME( JOBW, 'N' ) .OR. RIGHTW ) ) THEN
INFO = -2
ELSE IF( .NOT. ( INVFR .OR. AUTOM .OR. LSAME( JOBINV, 'I' ) ) )
$ THEN
INFO = -3
ELSE IF( .NOT. ( LSAME( DICO, 'C' ) .OR. DISCR ) ) THEN
INFO = -4
ELSE IF( .NOT. ( LSAME( EQUIL, 'S' ) .OR.
$ LSAME( EQUIL, 'N' ) ) ) THEN
INFO = -5
ELSE IF( .NOT. ( FIXORD .OR. LSAME( ORDSEL, 'A' ) ) ) THEN
INFO = -6
ELSE IF( N.LT.0 ) THEN
INFO = -7
ELSE IF( NV.LT.0 ) THEN
INFO = -8
ELSE IF( NW.LT.0 ) THEN
INFO = -9
ELSE IF( M.LT.0 ) THEN
INFO = -10
ELSE IF( P.LT.0 ) THEN
INFO = -11
ELSE IF( FIXORD .AND. ( NR.LT.0 .OR. NR.GT.N ) ) THEN
INFO = -12
ELSE IF( ( DISCR .AND. ( ALPHA.LT.ZERO .OR. ALPHA.GT.ONE ) ) .OR.
$ ( .NOT.DISCR .AND. ALPHA.GT.ZERO ) ) THEN
INFO = -13
ELSE IF( LDA.LT.MAX( 1, N ) ) THEN
INFO = -15
ELSE IF( LDB.LT.MAX( 1, N ) ) THEN
INFO = -17
ELSE IF( LDC.LT.MAX( 1, P ) ) THEN
INFO = -19
ELSE IF( LDD.LT.MAX( 1, P ) ) THEN
INFO = -21
ELSE IF( LDAV.LT.1 .OR. ( LEFTW .AND. LDAV.LT.NV ) ) THEN
INFO = -23
ELSE IF( LDBV.LT.1 .OR. ( LEFTW .AND. LDBV.LT.NV ) ) THEN
INFO = -25
ELSE IF( LDCV.LT.1 .OR. ( LEFTW .AND. LDCV.LT.P ) ) THEN
INFO = -27
ELSE IF( LDDV.LT.1 .OR. ( LEFTW .AND. LDDV.LT.P ) ) THEN
INFO = -29
ELSE IF( LDAW.LT.1 .OR. ( RIGHTW .AND. LDAW.LT.NW ) ) THEN
INFO = -31
ELSE IF( LDBW.LT.1 .OR. ( RIGHTW .AND. LDBW.LT.NW ) ) THEN
INFO = -33
ELSE IF( LDCW.LT.1 .OR. ( RIGHTW .AND. LDCW.LT.M ) ) THEN
INFO = -35
ELSE IF( LDDW.LT.1 .OR. ( RIGHTW .AND. LDDW.LT.M ) ) THEN
INFO = -37
ELSE IF( TOL1.GE.ONE ) THEN
INFO = -40
ELSE IF( ( TOL2.GT.ZERO .AND. .NOT.FIXORD .AND. TOL2.GT.TOL1 )
$ .OR. TOL2.GE.ONE ) THEN
INFO = -41
ELSE IF( LDWORK.LT.LW ) THEN
INFO = -44
END IF
C
IF( INFO.NE.0 ) THEN
C
C Error return.
C
CALL XERBLA( 'AB09JD', -INFO )
RETURN
END IF
C
C Quick return if possible.
C
IF( MIN( N, M, P ).EQ.0 ) THEN
NR = 0
NS = 0
DWORK(1) = ONE
RETURN
END IF
C
IF( LSAME( EQUIL, 'S' ) ) THEN
C
C Scale simultaneously the matrices A, B and C:
C A <- inv(D)*A*D, B <- inv(D)*B and C <- C*D, where D is a
C diagonal matrix.
C Workspace: N.
C
MAXRED = C100
CALL TB01ID( 'All', N, M, P, MAXRED, A, LDA, B, LDB, C, LDC,
$ DWORK, INFO )
END IF
C
C Correct the value of ALPHA to ensure stability.
C
ALPWRK = ALPHA
SQREPS = SQRT( DLAMCH( 'E' ) )
IF( DISCR ) THEN
IF( ALPHA.EQ.ONE ) ALPWRK = ONE - SQREPS
ELSE
IF( ALPHA.EQ.ZERO ) ALPWRK = -SQREPS
END IF
C
C Allocate working storage.
C
KU = 1
KL = KU + N*N
KI = KL + N
KW = KI + N
C
C Compute an additive decomposition G = G1 + G2, where G1
C is the ALPHA-stable projection of G.
C
C Reduce A to a block-diagonal real Schur form, with the NU-th order
C ALPHA-unstable part in the leading diagonal position, using a
C non-orthogonal similarity transformation A <- inv(T)*A*T and
C apply the transformation to B and C: B <- inv(T)*B and C <- C*T.
C
C Workspace needed: N*(N+2);
C Additional workspace: need 3*N;
C prefer larger.
C
CALL TB01KD( DICO, 'Unstable', 'General', N, M, P, ALPWRK, A, LDA,
$ B, LDB, C, LDC, NU, DWORK(KU), N, DWORK(KL),
$ DWORK(KI), DWORK(KW), LDWORK-KW+1, IERR )
C
IF( IERR.NE.0 ) THEN
IF( IERR.NE.3 ) THEN
INFO = 1
ELSE
INFO = 2
END IF
RETURN
END IF
C
WRKOPT = DWORK(KW) + DBLE( KW-1 )
IWARNL = 0
C
NS = N - NU
IF( FIXORD ) THEN
NRA = MAX( 0, NR-NU )
IF( NR.LT.NU )
$ IWARNL = 2
ELSE
NRA = 0
END IF
C
C Finish if only unstable part is present.
C
IF( NS.EQ.0 ) THEN
NR = NU
DWORK(1) = WRKOPT
RETURN
END IF
C
NU1 = NU + 1
IF( CONJV ) THEN
JOBVL = 'C'
ELSE
JOBVL = 'V'
END IF
IF( CONJW ) THEN
JOBWL = 'C'
ELSE
JOBWL = 'W'
END IF
IF( LEFTW ) THEN
C
C Check if V is invertible.
C Real workspace: need (NV+P)**2 + MAX( P + MAX(3*P,NV),
C MIN(P+1,NV) + MAX(3*(P+1),NV+P) );
C prefer larger.
C Integer workspace: need 2*NV+P+2.
C
TOL = ZERO
CALL AB08MD( 'S', NV, P, P, AV, LDAV, BV, LDBV, CV, LDCV,
$ DV, LDDV, RANK, TOL, IWORK, DWORK, LDWORK,
$ IERR )
IF( RANK.NE.P ) THEN
INFO = 20
RETURN
END IF
WRKOPT = MAX( WRKOPT, DWORK(1) )
C
IF( LEFTI ) THEN
IF( INVFR ) THEN
IERR = 1
ELSE
C
C Allocate storage for a standard inverse of V.
C Workspace: need NV*(NV+2*P) + P*P.
C
KAV = 1
KBV = KAV + NV*NV
KCV = KBV + NV*P
KDV = KCV + P*NV
KW = KDV + P*P
C
LDABV = MAX( NV, 1 )
LDCDV = P
CALL DLACPY( 'Full', NV, NV, AV, LDAV,
$ DWORK(KAV), LDABV )
CALL DLACPY( 'Full', NV, P, BV, LDBV,
$ DWORK(KBV), LDABV )
CALL DLACPY( 'Full', P, NV, CV, LDCV,
$ DWORK(KCV), LDCDV )
CALL DLACPY( 'Full', P, P, DV, LDDV,
$ DWORK(KDV), LDCDV )
C
C Compute the standard inverse of V.
C Additional real workspace: need MAX(1,4*P);
C prefer larger.
C Integer workspace: need 2*P.
C
CALL AB07ND( NV, P, DWORK(KAV), LDABV, DWORK(KBV), LDABV,
$ DWORK(KCV), LDCDV, DWORK(KDV), LDCDV,
$ RCOND, IWORK, DWORK(KW), LDWORK-KW+1, IERR )
WRKOPT = MAX( WRKOPT, DWORK(KW) + DBLE( KW-1 ) )
C
C Check if inversion is accurate.
C
IF( AUTOM ) THEN
IF( IERR.EQ.0 .AND. RCOND.LE.P0001 ) IERR = 1
ELSE
IF( IERR.EQ.0 .AND. RCOND.LE.SQREPS ) IERR = 1
END IF
IF( IERR.NE.0 .AND. NV.EQ.0 ) THEN
INFO = 20
RETURN
END IF
END IF
C
IF( IERR.NE.0 ) THEN
C
C Allocate storage for a descriptor inverse of V.
C
KAV = 1
KEV = KAV + NVP*NVP
KBV = KEV + NVP*NVP
KCV = KBV + NVP*P
KDV = KCV + P*NVP
KW = KDV + P*P
C
LDABV = MAX( NVP, 1 )
LDCDV = P
C
C DV is singular or ill-conditioned.
C Form a descriptor inverse of V.
C Workspace: need 2*(NV+P)*(NV+2*P) + P*P.
C
CALL AG07BD( 'I', NV, P, AV, LDAV, TEMP, 1, BV, LDBV,
$ CV, LDCV, DV, LDDV, DWORK(KAV), LDABV,
$ DWORK(KEV), LDABV, DWORK(KBV), LDABV,
$ DWORK(KCV), LDCDV, DWORK(KDV), LDCDV, IERR )
C
C Compute the projection containing the poles of weighted
C reduced ALPHA-stable part using descriptor inverse of V
C of order NVP = NV + P.
C Additional real workspace: need
C MAX( 2*NVP*NVP + MAX( 11*NVP+16, P*NVP ),
C NVP*N + MAX( NVP*N+N*N, P*N, P*M ) );
C prefer larger.
C Integer workspace: need NVP+N+6.
C
CALL AB09JV( JOBVL, DICO, 'G', 'C', NS, M, P, NVP, P,
$ A(NU1,NU1), LDA, B(NU1,1), LDB,
$ C(1,NU1), LDC, D, LDD,
$ DWORK(KAV), LDABV, DWORK(KEV), LDABV,
$ DWORK(KBV), LDABV, DWORK(KCV), LDCDV,
$ DWORK(KDV), LDCDV, IWORK, DWORK(KW),
$ LDWORK-KW+1, IERR )
IF( IERR.NE.0 ) THEN
IF( IERR.EQ.1 ) THEN
INFO = 5
ELSE IF( IERR.EQ.2 ) THEN
INFO = 16
ELSE IF( IERR.EQ.4 ) THEN
INFO = 18
END IF
RETURN
END IF
ELSE
C
C Compute the projection containing the poles of weighted
C reduced ALPHA-stable part using explicit inverse of V.
C Additional real workspace: need
C MAX( NV*(NV+5), NV*N + MAX( a, P*N, P*M ) )
C a = 0, if DICO = 'C' or JOBVL = 'V',
C a = 2*NV, if DICO = 'D' and JOBVL = 'C';
C prefer larger.
C
CALL AB09JV( JOBVL, DICO, 'I', 'C', NS, M, P, NV, P,
$ A(NU1,NU1), LDA, B(NU1,1), LDB,
$ C(1,NU1), LDC, D, LDD, DWORK(KAV), LDABV,
$ TEMP, 1, DWORK(KBV), LDABV,
$ DWORK(KCV), LDCDV, DWORK(KDV), LDCDV, IWORK,
$ DWORK(KW), LDWORK-KW+1, IERR )
IF( IERR.NE.0 ) THEN
IF( IERR.EQ.1 ) THEN
INFO = 10
ELSE IF( IERR.EQ.3 ) THEN
INFO = 14
ELSE IF( IERR.EQ.4 ) THEN
INFO = 18
END IF
RETURN
END IF
END IF
C
WRKOPT = MAX( WRKOPT, DWORK(KW) + DBLE( KW - 1 ) )
ELSE
C
C Compute the projection of V*G1 or conj(V)*G1 containing the
C poles of G.
C
C Workspace need:
C real MAX( 1, NV*(NV+5), NV*N + MAX( a, P*N, P*M ) )
C a = 0, if DICO = 'C' or JOBVL = 'V',
C a = 2*NV, if DICO = 'D' and JOBVL = 'C';
C prefer larger.
C
CALL AB09JV( JOBVL, DICO, 'I', 'C', NS, M, P, NV, P,
$ A(NU1,NU1), LDA, B(NU1,1), LDB,
$ C(1,NU1), LDC, D, LDD, AV, LDAV,
$ TEMP, 1, BV, LDBV, CV, LDCV, DV, LDDV, IWORK,
$ DWORK, LDWORK, IERR )
IF( IERR.NE.0 ) THEN
IF( IERR.EQ.1 ) THEN
INFO = 3
ELSE IF( IERR.EQ.3 ) THEN
INFO = 12
ELSE IF( IERR.EQ.4 ) THEN
INFO = 18
END IF
RETURN
END IF
C
WRKOPT = MAX( WRKOPT, DWORK(1) )
END IF
END IF
C
IF( RIGHTW ) THEN
C
C Check if W is invertible.
C Real workspace: need (NW+M)**2 + MAX( M + MAX(3*M,NW),
C MIN(M+1,NW) + MAX(3*(M+1),NW+M) );
C prefer larger.
C Integer workspace: need 2*NW+M+2.
C
TOL = ZERO
CALL AB08MD( 'S', NW, M, M, AW, LDAW, BW, LDBW, CW, LDCW,
$ DW, LDDW, RANK, TOL, IWORK, DWORK, LDWORK,
$ IERR )
IF( RANK.NE.M ) THEN
INFO = 21
RETURN
END IF
WRKOPT = MAX( WRKOPT, DWORK(1) )
C
IF( RIGHTI ) THEN
IF( INVFR ) THEN
IERR = 1
ELSE
C
C Allocate storage for a standard inverse of W.
C Workspace: need NW*(NW+2*M) + M*M.
C
KAW = 1
KBW = KAW + NW*NW
KCW = KBW + NW*M
KDW = KCW + M*NW
KW = KDW + M*M
C
LDABW = MAX( NW, 1 )
LDCDW = M
CALL DLACPY( 'Full', NW, NW, AW, LDAW,
$ DWORK(KAW), LDABW )
CALL DLACPY( 'Full', NW, M, BW, LDBW,
$ DWORK(KBW), LDABW )
CALL DLACPY( 'Full', M, NW, CW, LDCW,
$ DWORK(KCW), LDCDW )
CALL DLACPY( 'Full', M, M, DW, LDDW,
$ DWORK(KDW), LDCDW )
C
C Compute the standard inverse of W.