Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Giesl, Peter; Mohammed, Najla (2015)
Publisher: American Institute of Mathematical Sciences
Languages: English
Types: Article
Subjects: QA
Lyapunov functions are a main tool to determine the domain of attraction of equilibria in dynamical systems. Recently, several methods have been presented to construct a Lyapunov function for a given system. In this paper, we improve the construction method for Lyapunov functions using Radial Basis Functions. We combine this method with a new grid refinement algorithm based on Voronoi diagrams. Starting with a coarse grid and applying the refinement algorithm, we thus manage to reduce the number of data points needed to construct Lyapunov functions. Finally, we give numerical examples to illustrate our algorithms.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] M. Berg, O. Cheong, M. Kerveld, and M. Overmars. Computational geometry: Algorithms and Applications. Springer-Verlag, Berlin, 2008.
    • [2] M. D. Buhmann. Radial basis functions. In Acta numerica, 2000, volume 9 of Acta Numer., pages 1-38. Cambridge Univ. Press, Cambridge, 2000.
    • [3] F. Camilli, L. Gru¨ne, and F. Wirth. A generalization of Zubov's method to perturbed systems. SIAM J. Control Optim., 40(2):496-515, 2001.
    • [4] M. Dellnitz and O. Junge. Set oriented numerical methods for dynamical systems. In Handbook of dynamical systems, Vol. 2, pages 221-264. North-Holland, Amsterdam, 2002.
    • [5] M. Floater and A. Iske. Multistep scattered data interpolation using compactly supported Radial Basis Functions. J. Comput. Appl. Math., 73(1-2):65-78, 1996.
    • [6] P. Giesl. Construction of Global Lyapunov Functions Using Radial Basis Functions. Lecture Notes in Math. 1904, Springer, 2007.
    • [7] P. Giesl. Construction of a local and global Lyapunov function using Radial Basis Functions. IMA J. Appl. Math., 73(5):782-802, 2008.
    • [9] P. Giesl and S. Hafstein. Review on computational methods for Lyapunov functions. Discrete and Continuous Dynamical Systems - Series B, submitted.
    • [10] P. Giesl and H. Wendland. Meshless collocation: error estimates with application to Dynamical Systems. SIAM J. Numer. Anal., 45(4):1723-1741, 2007.
    • [11] L. Gru¨ne. An adaptive grid scheme for the discrete Hamilton-Jacobi-Bellman equation. Numer. Math., 75(3):319-337, 1997.
    • [12] L. Gru¨ne. Asymptotic behavior of dynamical and control systems under perturbation and discretization, volume 1783 of Lecture Notes in Mathematics. Springer-Verlag, Berlin, 2002.
    • [13] S. Hafstein. A constructive converse Lyapunov theorem on exponential stability. Discrete and Continuous Dynamical Systems - Series A, 10(3):657-678, 2004.
    • [14] S. Hafstein. An algorithm for constructing Lyapunov functions. Monograph. Electron. J. Diff. Eqns., 2007.
    • [15] C. S. Hsu. Cell-to-cell mapping, volume 64 of Applied Mathematical Sciences. Springer-Verlag, New York, 1987.
    • [16] A. Iske. On the construction of kernel-based adaptive particle methods in numerical flow simulation. In Recent developments in the numerics of nonlinear hyperbolic conservation laws, volume 120 of Notes Numer. Fluid Mech. Multidiscip. Des., pages 197-221. Springer, Heidelberg, 2013.
    • [17] S. Iyengar, K. Boroojeni, and N. Balakrishnan. Mathematical Theories of Distributed Sensor Networks. Springer, New York, 2014.
    • [18] Z. Jian. Development of Strong Form Methods with Applications in Computational Mechanics. PhD thesis: National University of Singapore, Singapore, 2008.
    • [19] C. Kellett. Converse Theorems in Lyapunov's Second Method. Discrete and Continuous Dynamical Systems - Series B, submitted.
    • [20] R. Klein. Concrete and Abstract Voronoi Diagrams. Lecture Notes in Computer Science. Springer-Verlag, Berlin, 1989.
    • [21] J. Massera. On Liapounoff's conditions of stability. Ann. of Math., 50(2):705-721, 1949.
    • [22] A. Papachristodoulou, J. Anderson, G. Valmorbida, S. Pranja, P. Seiler, and P. Parrilo. SOSTOOLS: Sum of Squares Optimization Toolbox for MATLAB. User's guide. Version 3.00 edition, 2013.
    • [23] P. Parrilo. Structured Semidefinite Programs and Semialgebraic Geometry Methods in Robustness and Optimiza. PhD thesis: California Institute of Technology Pasadena, California, 2000.
    • [24] F. Preparata and M. Shamos. Computational geometry. Texts and Monographs in Computer Science. Springer-Verlag, New York, 1985.
    • [25] J. Ruppert. A Delaunay refinement algorithm for quality 2-dimensional mesh generation. J. Approx. Theory, 18(3):548-585, 1995.
    • [26] R. Sibson. Development of strong form methods with applications in computational mechanics. In V. Barnett, editor, Interpolating Multivariate data, chapter 2. John Wiley and Sons, New York, 1981.
    • [27] H. Wendland. Error estimates for interpolation by compactly supported Radial Basis Functions of minimal degree. J. Approx. Theory, 93:258-272, 1998.
    • [28] H. Wendland. Scattered data approximation, volume 17 of Cambridge Monographs on Applied and Computational Mathematics. Cambridge University Press, Cambridge, 2005.
    • [29] X. Zhang, R. Ding, and Y. Li. Adaptive RPIM meshless method. In Proceedings of the 2011 International Conference on Multimedia Technology (ICMT), pages 2388-2392. IEEE, 2011. E-mail address: ,
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article