X = Matrices.continuousLyapunov(A, C); X = Matrices.continuousLyapunov(A, C, ATisSchur, eps);
This function computes the solution X of the continuous-time Lyapunov equation
X*A + A'*X = C
using the Schur method for Lyapunov equations proposed by Bartels and Stewart [1].
In a nutshell, the problem is reduced to the corresponding problem
Y*R' + R*Y = D
with
R=U'*A'*U
is the real Schur form of A' and
D=U'*C*U
and
Y=U'*X*U
are the corresponding transformations of C and
X. This problem is solved sequentially for the 1x1
or 2x2 Schur blocks by exploiting the block triangular form of
R. Finally the solution of the original problem is
recovered as
X=U*Y*U'.
The Boolean input "ATisSchur" indicates to omit the transformation
to Schur in the case that A' has already Schur
form.
[1] Bartels, R.H. and Stewart G.W. Algorithm 432: Solution of the matrix equation AX + XB = C. Comm. ACM., Vol. 15, pp. 820-826, 1972.
A = [1, 2, 3, 4; 3, 4, 5, -2; -1, 2, -3, -5; 0, 2, 0, 6]; C = [-2, 3, 1, 0; -6, 8, 0, 1; 2, 3, 4, 5; 0, -2, 0, 0]; X = continuousLyapunov(A, C); results in: X = [1.633, -0.761, 0.575, -0.656; -1.158, 1.216, 0.047, 0.343; -1.066, -0.052, -0.916, 1.61; -2.473, 0.717, -0.986, 1.48]
Matrices.continuousSylvester, Matrices.discreteLyapunov
function continuousLyapunov extends Modelica.Icons.Function; import Modelica.Math.Matrices; input Real A[:, size(A, 1)] "Square matrix A in X*A + A'*X = C"; input Real C[size(A, 1), size(A, 2)] "Square matrix C in X*A + A'*X = C"; input Boolean ATisSchur = false "True if transpose(A) has already real Schur form"; input Real eps = Modelica.Math.Matrices.norm(A, 1) * 10 * 1e-15 "Tolerance eps"; output Real X[size(A, 1), size(A, 2)] "Solution X of the Lyapunov equation X*A + A'*X = C"; end continuousLyapunov;