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<HTML>
<HEAD><TITLE>MB04ZD - SLICOT Library Routine Documentation</TITLE>
</HEAD>
<BODY>
<H2><A Name="MB04ZD">MB04ZD</A></H2>
<H3>
Transforming a Hamiltonian matrix into a square-reduced Hamiltonian matrix
</H3>
<A HREF ="#Specification"><B>[Specification]</B></A>
<A HREF ="#Arguments"><B>[Arguments]</B></A>
<A HREF ="#Method"><B>[Method]</B></A>
<A HREF ="#References"><B>[References]</B></A>
<A HREF ="#Comments"><B>[Comments]</B></A>
<A HREF ="#Example"><B>[Example]</B></A>
<P>
<B><FONT SIZE="+1">Purpose</FONT></B>
<PRE>
To transform a Hamiltonian matrix
( A G )
H = ( T ) (1)
( Q -A )
into a square-reduced Hamiltonian matrix
( A' G' )
H' = ( T ) (2)
( Q' -A' )
T
by an orthogonal symplectic similarity transformation H' = U H U,
where
( U1 U2 )
U = ( ). (3)
( -U2 U1 )
T
The square-reduced Hamiltonian matrix satisfies Q'A' - A' Q' = 0,
and
2 T 2 ( A'' G'' )
H' := (U H U) = ( T ).
( 0 A'' )
In addition, A'' is upper Hessenberg and G'' is skew symmetric.
The square roots of the eigenvalues of A'' = A'*A' + G'*Q' are the
eigenvalues of H.
</PRE>
<A name="Specification"><B><FONT SIZE="+1">Specification</FONT></B></A>
<PRE>
SUBROUTINE MB04ZD( COMPU, N, A, LDA, QG, LDQG, U, LDU, DWORK, INFO
$ )
C .. Scalar Arguments ..
INTEGER INFO, LDA, LDQG, LDU, N
CHARACTER COMPU
C .. Array Arguments ..
DOUBLE PRECISION A(LDA,*), DWORK(*), QG(LDQG,*), U(LDU,*)
</PRE>
<A name="Arguments"><B><FONT SIZE="+1">Arguments</FONT></B></A>
<P>
<B>Mode Parameters</B>
<PRE>
COMPU CHARACTER*1
Indicates whether the orthogonal symplectic similarity
transformation matrix U in (3) is returned or
accumulated into an orthogonal symplectic matrix, or if
the transformation matrix is not required, as follows:
= 'N': U is not required;
= 'I' or 'F': on entry, U need not be set;
on exit, U contains the orthogonal
symplectic matrix U from (3);
= 'V' or 'A': the orthogonal symplectic similarity
transformations are accumulated into U;
on input, U must contain an orthogonal
symplectic matrix S;
on exit, U contains S*U with U from (3).
See the description of U below for details.
</PRE>
<B>Input/Output Parameters</B>
<PRE>
N (input) INTEGER
The order of the matrices A, G, and Q. N >= 0.
A (input/output) DOUBLE PRECISION array, dimension (LDA,N)
On input, the leading N-by-N part of this array must
contain the upper left block A of the Hamiltonian matrix H
in (1).
On output, the leading N-by-N part of this array contains
the upper left block A' of the square-reduced Hamiltonian
matrix H' in (2).
LDA INTEGER
The leading dimension of the array A. LDA >= MAX(1,N).
QG (input/output) DOUBLE PRECISION array, dimension
(LDQG,N+1)
On input, the leading N-by-N lower triangular part of this
array must contain the lower triangle of the lower left
symmetric block Q of the Hamiltonian matrix H in (1), and
the N-by-N upper triangular part of the submatrix in the
columns 2 to N+1 of this array must contain the upper
triangle of the upper right symmetric block G of H in (1).
So, if i >= j, then Q(i,j) = Q(j,i) is stored in QG(i,j)
and G(i,j) = G(j,i) is stored in QG(j,i+1).
On output, the leading N-by-N lower triangular part of
this array contains the lower triangle of the lower left
symmetric block Q', and the N-by-N upper triangular part
of the submatrix in the columns 2 to N+1 of this array
contains the upper triangle of the upper right symmetric
block G' of the square-reduced Hamiltonian matrix H'
in (2).
LDQG INTEGER
The leading dimension of the array QG. LDQG >= MAX(1,N).
U (input/output) DOUBLE PRECISION array, dimension (LDU,2*N)
If COMPU = 'N', then this array is not referenced.
If COMPU = 'I' or 'F', then the input contents of this
array are not specified. On output, the leading
N-by-(2*N) part of this array contains the first N rows
of the orthogonal symplectic matrix U in (3).
If COMPU = 'V' or 'A', then, on input, the leading
N-by-(2*N) part of this array must contain the first N
rows of an orthogonal symplectic matrix S. On output, the
leading N-by-(2*N) part of this array contains the first N
rows of the product S*U where U is the orthogonal
symplectic matrix from (3).
The storage scheme implied by (3) is used for orthogonal
symplectic matrices, i.e., only the first N rows are
stored, as they contain all relevant information.
LDU INTEGER
The leading dimension of the array U.
LDU >= MAX(1,N), if COMPU <> 'N';
LDU >= 1, if COMPU = 'N'.
</PRE>
<B>Workspace</B>
<PRE>
DWORK DOUBLE PRECISION array, dimension (2*N)
</PRE>
<B>Error Indicator</B>
<PRE>
INFO INTEGER
= 0: successful exit;
< 0: if INFO = -i, then the i-th argument had an illegal
value.
</PRE>
<A name="Method"><B><FONT SIZE="+1">Method</FONT></B></A>
<PRE>
The Hamiltonian matrix H is transformed into a square-reduced
Hamiltonian matrix H' using the implicit version of Van Loan's
method as proposed in [1,2,3].
</PRE>
<A name="References"><B><FONT SIZE="+1">References</FONT></B></A>
<PRE>
[1] Van Loan, C. F.
A Symplectic Method for Approximating All the Eigenvalues of
a Hamiltonian Matrix.
Linear Algebra and its Applications, 61, pp. 233-251, 1984.
[2] Byers, R.
Hamiltonian and Symplectic Algorithms for the Algebraic
Riccati Equation.
Ph. D. Thesis, Cornell University, Ithaca, NY, January 1983.
[3] Benner, P., Byers, R., and Barth, E.
Fortran 77 Subroutines for Computing the Eigenvalues of
Hamiltonian Matrices. I: The Square-Reduced Method.
ACM Trans. Math. Software, 26, 1, pp. 49-77, 2000.
</PRE>
<A name="Numerical Aspects"><B><FONT SIZE="+1">Numerical Aspects</FONT></B></A>
<PRE>
This algorithm requires approximately 20*N**3 flops for
transforming H into square-reduced form. If the transformations
are required, this adds another 8*N**3 flops. The method is
strongly backward stable in the sense that if H' and U are the
computed square-reduced Hamiltonian and computed orthogonal
symplectic similarity transformation, then there is an orthogonal
symplectic matrix T and a Hamiltonian matrix M such that
H T = T M
|| T - U || <= c1 * eps
|| H' - M || <= c2 * eps * || H ||
where c1, c2 are modest constants depending on the dimension N and
eps is the machine precision.
Eigenvalues computed by explicitly forming the upper Hessenberg
matrix A'' = A'A' + G'Q', with A', G', and Q' as in (2), and
applying the Hessenberg QR iteration to A'' are exactly
eigenvalues of a perturbed Hamiltonian matrix H + E, where
|| E || <= c3 * sqrt(eps) * || H ||,
and c3 is a modest constant depending on the dimension N and eps
is the machine precision. Moreover, if the norm of H and an
eigenvalue lambda are of roughly the same magnitude, the computed
eigenvalue is essentially as accurate as the computed eigenvalue
from traditional methods. See [1] or [2].
</PRE>
<A name="Comments"><B><FONT SIZE="+1">Further Comments</FONT></B></A>
<PRE>
None
</PRE>
<A name="Example"><B><FONT SIZE="+1">Example</FONT></B></A>
<P>
<B>Program Text</B>
<PRE>
* MB04ZD EXAMPLE PROGRAM TEXT.
*
* .. Parameters ..
INTEGER NIN, NOUT
PARAMETER ( NIN = 5, NOUT = 6 )
INTEGER NMAX
PARAMETER ( NMAX = 20 )
INTEGER LDA, LDQG, LDU
PARAMETER ( LDA = NMAX, LDQG = NMAX, LDU = NMAX )
INTEGER LDWORK
PARAMETER ( LDWORK = ( NMAX+NMAX )*( NMAX+NMAX+1 ) )
DOUBLE PRECISION ZERO, ONE
PARAMETER ( ZERO = 0.0D0, ONE = 1.0D0 )
* .. Local Scalars ..
INTEGER I, INFO, IJ, J, JI, N, POS, WPOS
CHARACTER*1 COMPU
* .. Local Arrays ..
DOUBLE PRECISION A(LDA,NMAX), DWORK(LDWORK), QG(LDQG,NMAX+1),
$ U(LDU,NMAX)
* .. External Subroutines ..
EXTERNAL DCOPY, DGEMM, DSYMV, MB04ZD
* .. Executable Statements ..
*
WRITE ( NOUT, FMT = 99999 )
* Skip the heading in the data file and read the data.
READ ( NIN, FMT = '()' )
READ ( NIN, FMT = * ) N, COMPU
IF ( N.LT.0 .OR. N.GT.NMAX ) THEN
WRITE ( NOUT, FMT = 99998 ) N
ELSE
READ ( NIN, FMT = * ) ( ( A(I,J), J = 1,N ), I = 1,N )
READ ( NIN, FMT = * ) ( ( QG(J,I+1), I = J,N ), J = 1,N )
READ ( NIN, FMT = * ) ( ( QG(I,J), I = J,N ), J = 1,N )
* Square-reduce by symplectic orthogonal similarity.
CALL MB04ZD( COMPU, N, A, LDA, QG, LDQG, U, LDU, DWORK, INFO )
IF ( INFO.NE.0 ) THEN
WRITE ( NOUT, FMT = 99997 ) INFO
ELSE
* Show the square-reduced Hamiltonian.
WRITE ( NOUT, FMT = 99996 )
DO 10 I = 1, N
WRITE ( NOUT, FMT = 99994 ) ( A(I,J), J = 1,N ),
$ ( QG(J,I+1), J = 1,I-1 ), ( QG(I,J+1), J = I,N )
10 CONTINUE
DO 20 I = 1, N
WRITE ( NOUT, FMT = 99994 ) ( QG(I,J), J = 1,I-1 ),
$ ( QG(J,I), J = I,N ), ( -A(J,I), J = 1,N )
20 CONTINUE
* Show the square of H.
WRITE ( NOUT, FMT = 99995 )
WPOS = ( NMAX+NMAX )*( NMAX+NMAX )
* T
* Compute N11 = A*A + G*Q and set N22 = N11 .
CALL DGEMM( 'N', 'N', N, N, N, ONE, A, LDA, A, LDA, ZERO,
$ DWORK, N+N )
DO 30 I = 1, N
CALL DCOPY( N-I+1, QG(I,I), 1, DWORK(WPOS+I), 1 )
CALL DCOPY( I-1, QG(I,1), LDQG, DWORK(WPOS+1), 1 )
CALL DSYMV( 'U', N, ONE, QG(1,2), LDQG, DWORK(WPOS+1), 1,
$ ONE, DWORK((I-1)*(N+N)+1), 1 )
POS = N*( N+N ) + N + I
CALL DCOPY( N, DWORK((I-1)*(N+N)+1), 1, DWORK(POS), N+N )
30 CONTINUE
DO 40 I = 1, N
CALL DSYMV( 'U', N, -ONE, QG(1,2), LDQG, A(I,1), LDA,
$ ZERO, DWORK((N+I-1)*(N+N)+1), 1 )
CALL DSYMV( 'L', N, ONE, QG, LDQG, A(1,I), 1, ZERO,
$ DWORK((I-1)*(N+N)+N+1), 1 )
40 CONTINUE
DO 60 J = 1, N
DO 50 I = J, N
IJ = ( N+J-1 )*( N+N ) + I
JI = ( N+I-1 )*( N+N ) + J
DWORK(IJ) = DWORK(IJ) - DWORK(JI)
DWORK(JI) = -DWORK(IJ)
IJ = N + I + ( J-1 )*( N+N )
JI = N + J + ( I-1 )*( N+N )
DWORK(IJ) = DWORK(IJ) - DWORK(JI)
DWORK(JI) = -DWORK(IJ)
50 CONTINUE
60 CONTINUE
DO 70 I = 1, N+N
WRITE ( NOUT, FMT = 99994 )
$ ( DWORK(I+(J-1)*(N+N) ), J = 1,N+N )
70 CONTINUE
ENDIF
END IF
STOP
*
99999 FORMAT (' MB04ZD EXAMPLE PROGRAM RESULTS',/1X)
99998 FORMAT (/' N is out of range.',/' N = ',I5)
99997 FORMAT (' INFO on exit from MB04ZD = ',I2)
99996 FORMAT (/' The square-reduced Hamiltonian is ')
99995 FORMAT (/' The square of the square-reduced Hamiltonian is ')
99994 FORMAT (1X,8(F10.4))
END
</PRE>
<B>Program Data</B>
<PRE>
MB04ZD EXAMPLE PROGRAM DATA
3 N
1.0 2.0 3.0
4.0 5.0 6.0
7.0 8.0 9.0
1.0 1.0 1.0 2.0 2.0 3.0
7.0 6.0 5.0 8.0 4.0 9.0
</PRE>
<B>Program Results</B>
<PRE>
MB04ZD EXAMPLE PROGRAM RESULTS
The square-reduced Hamiltonian is
1.0000 3.3485 0.3436 1.0000 1.9126 -0.1072
6.7566 11.0750 -0.3014 1.9126 8.4479 -1.0790
2.3478 1.6899 -2.3868 -0.1072 -1.0790 -2.9871
7.0000 8.6275 -0.6352 -1.0000 -6.7566 -2.3478
8.6275 16.2238 -0.1403 -3.3485 -11.0750 -1.6899
-0.6352 -0.1403 1.2371 -0.3436 0.3014 2.3868
The square of the square-reduced Hamiltonian is
48.0000 80.6858 -2.5217 0.0000 1.8590 -10.5824
167.8362 298.4815 -4.0310 -1.8590 0.0000 -33.1160
0.0000 4.5325 2.5185 10.5824 33.1160 0.0000
0.0000 0.0000 0.0000 48.0000 167.8362 0.0000
0.0000 0.0000 0.0000 80.6858 298.4815 4.5325
0.0000 0.0000 0.0000 -2.5217 -4.0310 2.5185
</PRE>
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