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<HTML>
<HEAD><TITLE>SB03OU - SLICOT Library Routine Documentation</TITLE>
</HEAD>
<BODY>
<H2><A Name="SB03OU">SB03OU</A></H2>
<H3>
Solving (for Cholesky factor) stable continuous- or discrete-time Lyapunov equations, with matrix A in real Schur form and B rectangular
</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 solve for X = op(U)'*op(U) either the stable non-negative
definite continuous-time Lyapunov equation
2
op(A)'*X + X*op(A) = -scale *op(B)'*op(B) (1)
or the convergent non-negative definite discrete-time Lyapunov
equation
2
op(A)'*X*op(A) - X = -scale *op(B)'*op(B) (2)
where op(K) = K or K' (i.e., the transpose of the matrix K), A is
an N-by-N matrix in real Schur form, op(B) is an M-by-N matrix,
U is an upper triangular matrix containing the Cholesky factor of
the solution matrix X, X = op(U)'*op(U), and scale is an output
scale factor, set less than or equal to 1 to avoid overflow in X.
If matrix B has full rank then the solution matrix X will be
positive-definite and hence the Cholesky factor U will be
nonsingular, but if B is rank deficient then X may only be
positive semi-definite and U will be singular.
In the case of equation (1) the matrix A must be stable (that
is, all the eigenvalues of A must have negative real parts),
and for equation (2) the matrix A must be convergent (that is,
all the eigenvalues of A must lie inside the unit circle).
</PRE>
<A name="Specification"><B><FONT SIZE="+1">Specification</FONT></B></A>
<PRE>
SUBROUTINE SB03OU( DISCR, LTRANS, N, M, A, LDA, B, LDB, TAU, U,
$ LDU, SCALE, DWORK, LDWORK, INFO )
C .. Scalar Arguments ..
LOGICAL DISCR, LTRANS
INTEGER INFO, LDA, LDB, LDU, LDWORK, M, N
DOUBLE PRECISION SCALE
C .. Array Arguments ..
DOUBLE PRECISION A(LDA,*), B(LDB,*), DWORK(*), TAU(*), U(LDU,*)
</PRE>
<A name="Arguments"><B><FONT SIZE="+1">Arguments</FONT></B></A>
<P>
<B>Mode Parameters</B>
<PRE>
DISCR LOGICAL
Specifies the type of Lyapunov equation to be solved as
follows:
= .TRUE. : Equation (2), discrete-time case;
= .FALSE.: Equation (1), continuous-time case.
LTRANS LOGICAL
Specifies the form of op(K) to be used, as follows:
= .FALSE.: op(K) = K (No transpose);
= .TRUE. : op(K) = K**T (Transpose).
</PRE>
<B>Input/Output Parameters</B>
<PRE>
N (input) INTEGER
The order of the matrix A and the number of columns in
matrix op(B). N >= 0.
M (input) INTEGER
The number of rows in matrix op(B). M >= 0.
A (input) DOUBLE PRECISION array, dimension (LDA,N)
The leading N-by-N upper Hessenberg part of this array
must contain a real Schur form matrix S. The elements
below the upper Hessenberg part of the array A are not
referenced. The 2-by-2 blocks must only correspond to
complex conjugate pairs of eigenvalues (not to real
eigenvalues).
LDA INTEGER
The leading dimension of array A. LDA >= MAX(1,N).
B (input/output) DOUBLE PRECISION array, dimension (LDB,N)
if LTRANS = .FALSE., and dimension (LDB,M), if
LTRANS = .TRUE..
On entry, if LTRANS = .FALSE., the leading M-by-N part of
this array must contain the coefficient matrix B of the
equation.
On entry, if LTRANS = .TRUE., the leading N-by-M part of
this array must contain the coefficient matrix B of the
equation.
On exit, if LTRANS = .FALSE., the leading
MIN(M,N)-by-MIN(M,N) upper triangular part of this array
contains the upper triangular matrix R (as defined in
METHOD), and the M-by-MIN(M,N) strictly lower triangular
part together with the elements of the array TAU are
overwritten by details of the matrix P (also defined in
METHOD). When M < N, columns (M+1),...,N of the array B
are overwritten by the matrix Z (see METHOD).
On exit, if LTRANS = .TRUE., the leading
MIN(M,N)-by-MIN(M,N) upper triangular part of
B(1:N,M-N+1), if M >= N, or of B(N-M+1:N,1:M), if M < N,
contains the upper triangular matrix R (as defined in
METHOD), and the remaining elements (below the diagonal
of R) together with the elements of the array TAU are
overwritten by details of the matrix P (also defined in
METHOD). When M < N, rows 1,...,(N-M) of the array B
are overwritten by the matrix Z (see METHOD).
LDB INTEGER
The leading dimension of array B.
LDB >= MAX(1,M), if LTRANS = .FALSE.,
LDB >= MAX(1,N), if LTRANS = .TRUE..
TAU (output) DOUBLE PRECISION array of dimension (MIN(N,M))
This array contains the scalar factors of the elementary
reflectors defining the matrix P.
U (output) DOUBLE PRECISION array of dimension (LDU,N)
The leading N-by-N upper triangular part of this array
contains the Cholesky factor of the solution matrix X of
the problem, X = op(U)'*op(U).
The array U may be identified with B in the calling
statement, if B is properly dimensioned, and the
intermediate results returned in B are not needed.
LDU INTEGER
The leading dimension of array U. LDU >= MAX(1,N).
SCALE (output) DOUBLE PRECISION
The scale factor, scale, set less than or equal to 1 to
prevent the solution overflowing.
</PRE>
<B>Workspace</B>
<PRE>
DWORK DOUBLE PRECISION array, dimension (LDWORK)
On exit, if INFO = 0, or INFO = 1, DWORK(1) returns the
optimal value of LDWORK.
LDWORK INTEGER
The length of the array DWORK. LDWORK >= MAX(1,4*N).
For optimum performance LDWORK should sometimes be larger.
If LDWORK = -1, then a workspace query is assumed; the
routine only calculates the optimal size of the DWORK
array, returns this value as the first entry of the DWORK
array, and no error message related to LDWORK is issued by
XERBLA.
</PRE>
<B>Error Indicator</B>
<PRE>
INFO INTEGER
= 0: successful exit;
< 0: if INFO = -i, the i-th argument had an illegal
value;
= 1: if the Lyapunov equation is (nearly) singular
(warning indicator);
if DISCR = .FALSE., this means that while the matrix
A has computed eigenvalues with negative real parts,
it is only just stable in the sense that small
perturbations in A can make one or more of the
eigenvalues have a non-negative real part;
if DISCR = .TRUE., this means that while the matrix
A has computed eigenvalues inside the unit circle,
it is nevertheless only just convergent, in the
sense that small perturbations in A can make one or
more of the eigenvalues lie outside the unit circle;
perturbed values were used to solve the equation
(but the matrix A is unchanged);
= 2: if matrix A is not stable (that is, one or more of
the eigenvalues of A has a non-negative real part),
if DISCR = .FALSE., or not convergent (that is, one
or more of the eigenvalues of A lies outside the
unit circle), if DISCR = .TRUE.;
= 3: if matrix A has two or more consecutive non-zero
elements on the first sub-diagonal, so that there is
a block larger than 2-by-2 on the diagonal;
= 4: if matrix A has a 2-by-2 diagonal block with real
eigenvalues instead of a complex conjugate pair.
</PRE>
<A name="Method"><B><FONT SIZE="+1">Method</FONT></B></A>
<PRE>
The method used by the routine is based on the Bartels and
Stewart method [1], except that it finds the upper triangular
matrix U directly without first finding X and without the need
to form the normal matrix op(B)'*op(B) [2].
If LTRANS = .FALSE., the matrix B is factored as
B = P ( R ), M >= N, B = P ( R Z ), M < N,
( 0 )
(QR factorization), where P is an M-by-M orthogonal matrix and
R is a square upper triangular matrix.
If LTRANS = .TRUE., the matrix B is factored as
B = ( 0 R ) P, M >= N, B = ( Z ) P, M < N,
( R )
(RQ factorization), where P is an M-by-M orthogonal matrix and
R is a square upper triangular matrix.
These factorizations are used to solve the continuous-time
Lyapunov equation in the canonical form
2
op(A)'*op(U)'*op(U) + op(U)'*op(U)*op(A) = -scale *op(F)'*op(F),
or the discrete-time Lyapunov equation in the canonical form
2
op(A)'*op(U)'*op(U)*op(A) - op(U)'*op(U) = -scale *op(F)'*op(F),
where U and F are N-by-N upper triangular matrices, and
F = R, if M >= N, or
F = ( R ), if LTRANS = .FALSE., or
( 0 )
F = ( 0 Z ), if LTRANS = .TRUE., if M < N.
( 0 R )
The canonical equation is solved for U.
</PRE>
<A name="References"><B><FONT SIZE="+1">References</FONT></B></A>
<PRE>
[1] Bartels, R.H. and Stewart, G.W.
Solution of the matrix equation A'X + XB = C.
Comm. A.C.M., 15, pp. 820-826, 1972.
[2] Hammarling, S.J.
Numerical solution of the stable, non-negative definite
Lyapunov equation.
IMA J. Num. Anal., 2, pp. 303-325, 1982.
</PRE>
<A name="Numerical Aspects"><B><FONT SIZE="+1">Numerical Aspects</FONT></B></A>
<PRE> 3
The algorithm requires 0(N ) operations and is backward stable.
</PRE>
<A name="Comments"><B><FONT SIZE="+1">Further Comments</FONT></B></A>
<PRE>
The Lyapunov equation may be very ill-conditioned. In particular,
if A is only just stable (or convergent) then the Lyapunov
equation will be ill-conditioned. "Large" elements in U relative
to those of A and B, or a "small" value for scale, are symptoms
of ill-conditioning. A condition estimate can be computed using
SLICOT Library routine SB03MD.
</PRE>
<A name="Example"><B><FONT SIZE="+1">Example</FONT></B></A>
<P>
<B>Program Text</B>
<PRE>
None
</PRE>
<B>Program Data</B>
<PRE>
None
</PRE>
<B>Program Results</B>
<PRE>
None
</PRE>
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