Inverse Lyapunov Procedure

Description

The Inverse Lyapunov Procedure (ILP) is a synthetic random linear system generator. It is based on reversing the Balanced Truncation procedure and was developed in , where a description of the algorithm is given. In aggregate form, for randomly generated controllability and observability gramians, a balancing transformation is computed. The balanced gramian is the basis for an associated state-space system, which is determined by solving a Lyapunov equation and then unbalanced. A central point is the solution of the Lyapunov equations for the system matrix instead of the gramian matrix. This is feasable due to the symmetric (semi-)positive definiteness of the gramians and the requirement for a stable system, yet with a non-unique solution. Following, the steps of the ILP are listed:

1. Sample eigenvalues of controllability and observability Gramians.
2. Generate random orthogonal matrices (ie.: SVD of random matrix).
3. Compute balancing transformation for these Gramians.
4. Sample random input and output matrices.
5. Scale output matrix to input matrix.
6. Solve Lyapunov equation for system matrix.
7. Unbalance system.

Inverse Sylvester Procedure

A variant of the Inverse Lyapunov Procedure is the inverse Sylvester procedure (ILS) , which generates only state-space symmetric systems. Instead of balanced truncation, the cross Gramian is utilized for the random system generation, and hence a Sylvester equation is needs to be solved. The steps for the ILS are listed below:

1. Sample cross Gramian eigenvalues.
2. Sample random input matrix, and set output matrix as its transpose.
3. Solve Sylvester equation for system matrix.
4. Sample orthogonal unbalancing transformation (QR of random matrix).
5. Unbalance system.

Even though the ILS is more limited than the ILP, for large systems it can be more efficient.

Data

This benchmark is procedural and the input, state and output dimensions can be chosen. Use the following MATLAB code to generate a random system as described above:

function [A,B,C] = ilp(M,N,Q,s,r)
% ilp (inverse lyapunov procedure)
% by Christian Himpe, 2013--2018
% released under BSD 2-Clause License
%*
  if(nargin==5)
    rand('seed',r);
    randn('seed',r);
  end;

% Gramian Eigenvalues
  WC = exp(0.5*rand(N,1));
  WO = exp(0.5*rand(N,1));

% Gramian Eigenvectors
  [P,S,R] = svd(randn(N));

% Balancing Transformation
  WC = P*diag(sqrt(WC))*P';
  WO = R*diag(sqrt(WO))*R';
  [U,D,V] = svd(WC*WO);

% Input and Output
  B = randn(N,M);

  if(nargin>=4 && s~=0)
    C = B';
  else
    C = randn(Q,N);
  end

% Scale Output Matrix
  BB = sum(B.*B,2);  % = diag(B*B')
  CC = sum(C.*C,1)'; % = diag(C'*C)
  C = bsxfun(@times,C,sqrt(BB./CC)');

% Solve System Matrix
  A = -sylvester(D,D,B*B');

% Unbalance System
  A = V*A*U';
  B = V*B;
  C = C*U';

end

The function call requires three parameters; the number of inputs $M$, of states $N$ and outputs $Q$. Optionally, a symmetric system can be enforced with the parameter $s \neq 0$. For reproducibility, the random number generator seed can be controlled by the parameter $r \in \mathbb{N}$. The return value consists of three matrices; the system matrix $A$, the input matrix $B$ and the output matrix $C$.

[A,B,C] = ilp(M,N,Q,s,r);

A variant of the above code using empirical Gramians instead of a matrix equation solution can be found at http://gramian.de/utils/ilp.m , which may yield preferable results.

Dimensions

System structure:

$\begin{align} \dot{x}(t) &= Ax(t) + Bu(t) \\ y(t) &= Cx(t) \end{align}$

System dimensions:

$A \in \mathbb{R}^{N \times N}$, $B \in \mathbb{R}^{N \times M}$, $C \in \mathbb{R}^{Q \times N}$.

Citation

To cite this benchmark, use the following references:

• For the benchmark itself and its data:

The MORwiki Community, Inverse Lyapunov Procedure. MORwiki - Model Order Reduction Wiki, 2018. http://modelreduction.org/index.php/Inverse_Lyapunov_Procedure

@MISC{morwiki-invlyapproc,
  author =       {{The MORwiki Community}},
  title =        {Inverse Lyapunov Procedure},
  howpublished = {{MORwiki} -- Model Order Reduction Wiki},
  url =          {http://modelreduction.org/index.php/Inverse_Lyapunov_Procedure},
  year =         2018
}

• For the background on the benchmark:

@INPROCEEDINGS{SmiF03,
  author =       {Smith, S.~C. and Fisher, J.},
  title =        {On generating random systems: a gramian approach},
  booktitle =    {Proc. Am. Control. Conf.},
  volume =       3,
  pages =        {2743--2748},
  year =         2003,
  doi =          {10.1109/ACC.2003.1243494}
}

Contact

Christian Himpe