Iterative methods for solving large sparse Lyapunov equations and application to model reduction of index 1 differential-algebraic-equations

M. Sumon Hossain; M. Monir Uddin

doi:10.3934/naco.2019013

Article Contents

2019, Volume 9, Issue 2: 173-186. Doi: 10.3934/naco.2019013

This issue Previous Article A new reprojection of the conjugate directions Next Article Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm

Iterative methods for solving large sparse Lyapunov equations and application to model reduction of index 1 differential-algebraic-equations

M. Sumon Hossain^1, and
M. Monir Uddin^2, ,

1.
Department of Mathematics, Hamdard University Bangladesh, Gazaria, Munshiganj, Bangladesh
2.
Department of Mathematics and Physics, North South University, Bashundhara, Dhaka 1229, Bangladesh

Corresponding author: M. Monir Uddin (E-mail: monir.uddin@northsouth.edu)
Corresponding author: M. Monir Uddin (E-mail: monir.uddin@northsouth.edu)

Received: July 2017

Revised: July 2018

Published: January 2019

Abstract / Introduction Full Text(HTML) Figure(4) / Table(3) Related Papers Cited by

Abstract

To implement the balancing based model reduction of large-scale dynamical systems we need to compute the low-rank (controllability and observability) Gramian factors by solving Lyapunov equations. In recent time, Rational Krylov Subspace Method (RKSM) is considered as one of the efficient methods for solving the Lyapunov equations of large-scale sparse dynamical systems. The method is well established for solving the Lyapunov equations of the standard or generalized state space systems. In this paper, we develop algorithms for solving the Lyapunov equations for large-sparse structured descriptor system of index-1. The resulting algorithm is applied for the balancing based model reduction of large sparse power system model. Numerical results are presented to show the efficiency and capability of the proposed algorithm.

Keywords:

Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

Citation:

FullText(HTML)

Figure 1. Convergence histories of both Gramians by RKSM for mod-2

Download: Full-size image PowerPoint slide

Figure 2. Comparison between full system and reduced-order system in frequency domain

Download: Full-size image PowerPoint slide

Figure 3. Comparison between original system and reduced model in time domain

Download: Full-size image PowerPoint slide

Figure 4. Largest Hankel singular values of original system and 86 dimensional reduced-order system

Download: Full-size image PowerPoint slide

Table 1. Number of differential & algebraic variables and largest eigenvalue of $ (-A, E) $ for different models.

Model	differential	algebraic	eigs$ (-A, E) $	inputs/outputs
Mod-1	606	6 529	10727	4/4
Mod-2	1 142	8 593	10727	4/4
Mod-3	3 078	18 050	10669	4/4

| Show Table

DownLoad: CSV

Table 2. Comparisons between full systems and their reduced models

Model	Dimension			Error
	full	reduced		absolute	relative
	Mod-1	$ 7\, 135 $	$ 87 $		$ 3.1\times 10^{-3} $	$ 1.5 \times 10^{-4} $
Mod-2	$ 9\, 735 $	$ 86 $	$ 5.3 \times 10^{-2} $		$ 4.7 \times 10^{-4} $
Mod-3	$ 21\, 128 $	$ 77 $	$ 5.6 \times 10^{-1} $		$ 4.3 \times 10^{-2} $

| Show Table

DownLoad: CSV

Table 3. Balanced truncation tolerances and dimensions of reduced-order model.

Model	tolerance	dimension of ROM
	$ 10^{-4} $	118
	$ 10^{-3} $	104
Mod-2	$ 10^{-2} $	86
	$ 10^{-1} $	70

| Show Table

DownLoad: CSV

Related Papers

Cited by

References

[1]	A. C. Antoulas, Approximation of Large-Scale Dynamical Systems, SIAM Publications, Philadelphia, PA, 2005. doi: 10.1137/1.9780898718713.
[2]	R. Bartels and G. Stewart, Solution of the matrix equation AX+XB = C: Algorithm 432, Comm. ACM, 15 (1972), 820-826.
[3]	U. Baur, Control Oriented Model Reduction for Parabolic Systems, Ph.D thesis, Inst. f. Mathematik, Technische Universität Berlin, Berlin, 2008.
[4]	P. Benner, P. Kürschner and J. Saak, Self-generating and efficient shift parameters in ADI methods for large Lyapunov and Sylvester equations, Elect. Trans. Numer. Anal., 43 (2014), 142-162.
[5]	P. Benner, J. R. Li and T. Penzl, Numerical solution of large Lyapunov equations, Riccati equations, and linear-quadratic control problems, Numer. Lin. Alg. Appl., 15 (2008), 755-777. doi: 10.1002/nla.622.
[6]	P. Benner, E. Quintana-Ortí and G. Quintana-Ortí, A portable subroutine library for solving linear control problems on distributed memory computers, in Workshop on wide area networks and high performance computing, Lecture Notes in Control and Information, Springer-Verlag, Berlin/Heidelberg, Germany, (1999), 61–87. doi: 10.1007/BFb0110079.
[7]	P. Benner, E. Quintana-Ortí and G. Quintana-Ortí, Balanced truncation model reduction of large-scale dense systems on parallel computers, Math. Comput. Model. Dyn. Syst., 6 (2000), 383-405.
[8]	P. Benner, J. Saak and M. M. Uddin, Balancing based model reduction for structured index-2 unstable descriptor systems with application to flow control, Numer. Alg. Cont. Opt., 6 (2016), 1-20. doi: 10.3934/naco.2016.6.1.
[9]	T. A. Davis, Direct Methods for Sparse Linear Systems, SIAM Publications, Philadelphia, PA, 2006. doi: 10.1137/1.9780898718881.
[10]	V. Druskin and L. Knizhnerman, Extended Krylov subspaces: Approximation of the matrix square root and related functions, SIAM J. Matrix Anal. Appl., 19 (1998), 755-771. doi: 10.1137/S0895479895292400.
[11]	V. Druskin and V. Simoncini, Adaptive rational Krylov subspaces for large-scale dynamical systems, Systems & Control Letters, 60 (2011), 546–560. doi: 10.1016/j.sysconle.2011.04.013.
[12]	F. Freitas, J. Rommes and N. Martins, Gramian-based reduction method applied to large sparse power system descriptor models, IEEE Transactions on Power Systems, 23 (2008), 1258-1270.
[13]	R. W. Freund, Structure-preserving model order reduction of RCL circuit equations, in Model Order Reduction: Theory, Research Aspects and Applications, Springer-Verlag, (2008), 49–73. doi: 10.1007/978-3-540-78841-6_3.
[14]	K. Glover, All optimal Hankel-norm approximations of linear multivariable systems and their L^∞-error norms, Inter. J. Cont., 39 (1984), 1115-1193. doi: 10.1080/00207178408933239.
[15]	M. Green and D. J. N. Limebeer, Linear Robust Control, Prentice Hall, Englewood Cliffs, 1995.
[16]	S. Gugercin, D. Sorensen and A. Antoulas, A modified low-rank Smith method for large-scale Lyapunov equations, Numer. Alg., 32 (2003), 27-55. doi: 10.1023/A:1022205420182.
[17]	I. Jaimoukha and E. Kasenally, Krylov subspace methods for solving large Lyapunov equations, SIAM J. Numer. Anal., 31 (1994), 227-251. doi: 10.1137/0731012.
[18]	K. Jbilou, ADI preconditioned Krylov methods for large Lyapunov matrix equations, Lin. Alg. Appl., 432 (2010), 2473-2485. doi: 10.1016/j.laa.2009.12.025.
[19]	K. Jbilou and A.J. Riquet, Projection methods for large Lyapunov matrix equations, Lin. Alg. Appl., 415 (2006), 344-358. doi: 10.1016/j.laa.2004.11.004.
[20]	P. Kunkel and V. Mehrmann, Differential-Algebraic Equations: Analysis and Numerical Solution, Textbooks in Mathematics, EMS Publishing House, Zürich, Switzerland, 2006. doi: 10.4171/017.
[21]	J. R. Li, Model Reduction of Large Linear Systems via Low Rank System Gramians, Ph.D thesis, Massachusetts Institute of Technology, 2000.
[22]	J. R. Li and J. White, Low rank solution of Lyapunov equations, SIAM J. Matrix Anal. Appl., 24 (2002), 260-280. doi: 10.1137/S0895479801384937.
[23]	A. Lu and E. Wachspress, Solution of Lyapunov equations by alternating direction implicit iteration, Comput. Math. Appl., 21 (1991), 43-58. doi: 10.1016/0898-1221(91)90124-M.
[24]	T. Penzl, A cyclic low rank Smith method for large sparse Lyapunov equations, SIAM J. Sci. Comput., 21 (2000), 1401-1418. doi: 10.1137/S1064827598347666.
[25]	A. Ruhe, The rational Krylov algorithm for nonsymmetric eigenvalue problems. Ⅲ: Complex shifts for real matrices, BIT Numerical Mathematics, 34 (1994), 165-176. doi: 10.1007/BF01935024.
[26]	Y. Saad, Numerical solution of large Lyapunov equation, in Signal Processing, Scattering, Operator Theory and Numerical Methods (Amsterdam, 1989), of Prog. Syst. Cont. Theory, Birkhäuser, Bostan, MA, (1990), 503–511.
[27]	Y. Saad, Iterative Methods for Sparse Linear Systems, SIAM, Philadelphia, PA, USA, 2003. doi: 10.1137/1.9780898718003.
[28]	V. Simoncini, A new iterative method for solving large-scale Lyapunov matrix equations, SIAM J. Sci. Comput., 29 (2007), 1268-1288. doi: 10.1137/06066120X.
[29]	E. D. Sontag, Mathematical Control Theory, Springer, New York, 1998. doi: 10.1007/978-1-4612-0577-7.
[30]	M. M. Uddin, Model reduction for piezo-mechanical systems using Balanced Truncation, Master's thesis, Stockholm University, Stockholm, Sweden, 2011, Available from: http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-78227.
[31]	M. M. Uddin, J. Saak, B. Kranz and P. Benner, Computation of a compact state space model for an adaptive spindle head configuration with piezo actuators using balanced truncation, Springer-Verlag, Production Engineering, 6 (2012), 577-586. doi: 10.1007/s11740-012-0410-x.
[32]	M. M. Uddin, S. Hossain and F. Uddin, Rational Krylov subspace method (RKSM) for solving the Lyapunov equations of index-1 descriptor systems and application to balancing based model reduction, 9th International Conference on Electrical and Computer Engineering (ICECE) 2016, (2016), 451–454. doi: 10.1155/2017/4362641.