Abstract
In this work, we give a fast preconditioned numerical method to solve the discreted linear system, which is obtained from the nonlinear space fractional complex Ginzburg-Landau equation. The coefficient matrix of the discreted linear system is the sum of a complex diagonal matrix and a real Toeplitz matrix. The new method has a superiority in computation because we can use the circulant preconditioner and the fast Fourier transform (FFT) to solve the discreted linear system. Numerical examples are tested to illustrate the advantage of the preconditioned numerical method.
Supported by the “Peiyu” Project from Xuzhou University of Technology (Grant Number XKY2019104).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bunch, J.R.: Stability of methods for solving Toeplitz systems of equations. SIAM J. Sci. Stat. Comput. 6, 349–364 (1985)
Çelik, C., Duman, M.: Crank-Nicolson method for the fractional diffusion equation with the Riesz fractional derivative. J. Comput. Phys. 231, 1743–1750 (2012)
Chan, R., Jin, X.: An Introduction to Iterative Toeplitz Solvers. SIAM, Philadelphia (2007)
Chan, R., Ng, M.: Conjugate gradient methods for Toeplitz systems. SIAM Rev. 38, 427–482 (1996)
Chen, L., Jiang, D., Bao, R., Xiong, J., Liu, F., Bei, L.: MIMO Scheduling effectiveness analysis for bursty data service from view of QoE. Chin. J. Electron 26(5), 1079–1085 (2017)
Chen, L., Jiang, D., Song, H., Wang, P., Bao, R., Zhang, K., Li, Y.: A lightweight end-side user experience data collection system for quality evaluation of multimedia communications. IEEE Access 6(1), 15408–15419 (2018)
Chen, L., Zhang, L.: Spectral efficiency analysis for massive MIMO system Under QoS constraint: an effective capacity perspective. Mobile Netw. Appl. (2020). https://doi.org/10.1007/s11036-019-01414-4
Ching, W.-K.: Iterative Methods for Queuing and Manufacturing Systems. Springer-Verlag, London (2001)
Davis, P.: Circulant Matrices, 2nd edn. AMS Chelsea, Providence, RI (1994)
Gorenflo, R., Mainardi, F.: Random walk models for space-fractional diffusion processes. Fractional Calc. Appl. Anal. 1(2), 167–191 (1998)
Guo, B.-L., Huo, Z.-H.: Well-posedness for the nonlinear fractional Schrödinger equation and inviscid limit behavior of solution for the fractional Ginzburg-Landau equation. Fract. Calc. Appl. Anal. 16(1), 226–242 (2012)
Hansen, P.C., Nagy, J.G., O’Leary, D.P.: Deblurring Images: Matrices, Spectra, and Filtering. SIAM, Philadelphia (2006)
He, D., Pan, K.: An unconditionally stable linearized difference scheme for the fractional Ginzburg-Landau equation. Numer. Algor. 79, 899–925 (2018)
Huo, L., Jiang, D., Lv, Z., et al.: An intelligent optimization-based traffic information acquirement approach to software-defined networking. Comput. Intell. 1–21 (2019)
Huo, L., Jiang, D., Qi, S., et al.: An AI-based adaptive cognitive modeling and measurement method of network traffic for EIS. Mob. Netw. Appl. 1–11 (2019)
Huo, L., Jiang, D., Zhu, X., et al.: An SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic. Int. J. Commun. Syst. e4092 (2019)
Jiang, D., Huo, L., Lv, Z., et al.: A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans. Intell. Transp. Syst. 19(10), 3305–3319 (2018)
Jiang, D., Zhang, P., Lv, Z., et al.: Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet Things J. 3(6), 1437–1447 (2016)
Jiang, D., Li, W., Lv, H.: An energy-efficient cooperative multicast routing in multi-hop wireless networks for smart medical applications. Neurocomputing 220, 160–169 (2017)
Jiang, D., Wang, Y., Lv, Z., et al.: Intelligent Optimization-based reliable energy-efficient networking in cloud services for IIoT networks. IEEE J. Select. Areas Commun. (2019)
Jiang, D., Wang, W., Shi, L., et al.: A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Trans. Netw. Sci. Eng. 5(3), 1–12 (2018)
Jiang, D., Huo, L., Li, Y.: Fine-granularity inference and estimations to network traffic for SDN. Plos One 13(5), 1–23 (2018)
Jiang, D., Wang, Y., Lv, Z., et al.: Big data analysis-based network behavior insight of cellular networks for industry 4.0 applications. IEEE Trans. Ind. Inform. 16(2), 1310–1320 (2020)
Jiang, D., Huo, L., Song, H.: Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Trans. Netw. Sci. Eng. 1(1), 1–12 (2018)
Jin, X.-Q.: Developments and Applications of Block Toeplitz Iterative Solvers. The Netherlands, and Science Press, Beijing, China, Kluwer Academic Publishers, Dordrecht (2002)
Kailath, T., Sayed, A.H. (eds.): Fast Reliable Algorithms for Matrices with Structure. SIAM, Philadelphia (1999)
Milovanov, A., Rasmussen, J.: Fractional generalization of the Ginzburg-Landau equation: an unconventional approach to critical phenomena in complex media. Phys. Lett. 337, 75–80 (2005)
Mvogo, A., Tambue, A., Ben-Bolie, G., Kofane, T.: Localized numerical impulse solutions in diffuse neural networks modeled by the complex fractional Ginzburg-Landau equation. Commun. Nonlinear Sci. 39, 396–410 (2016)
Ng, M.K.: Iterative Methods for Toeplitz Systems. Oxford University Press, Oxford, UK (2004)
Pu, X., Guo, B.: Well-posedness and dynamics for the fractional Ginzburg-Landau equation. Appl. Anal. 92, 318–334 (2013)
Qi, S., Jiang, D., Huo, L.: A prediction approach to end-to-end traffic in space information networks. Mob. Netw. Appl. 1–10 (2019)
Saad, Y.: Iterative Methods for Sparse Linear Systems. SIAM, Philadelphia (2003)
Tarasov, V., Zaslavsky, G.: Fractional Ginzburg-Landau equation for fractal media. Phys. 354, 249–261 (2005)
Tarasov, V., Zaslavsky, G.: Fractional dynamics of coupled oscillators with long-range interaction. Chaos 16(2), 023110 (2006)
Tarasov, V.: Psi-series solution of fractional Ginzburg-Landau equation. J. Phys. A-Math. Gen. 39, 8395–8407 (2006)
Wang, P., Huang, C.: An implicit midpoint difference scheme for the fractional Ginzburg-Landau equation. J. Comput. Phys. 312, 31–49 (2016)
Wang, F., Jiang, D., Qi, S.: An adaptive routing algorithm for integrated information networks. China Commun. 7(1), 196–207 (2019)
Wang, F., Jiang, D., Qi, S., et al.: A dynamic resource scheduling scheme in edge computing satellite networks. Mob. Netw. Appl. 1–12 (2019)
Wang, Y., Jiang, D., Huo, L., et al.: A new traffic prediction algorithm to software defined networking. Mob. Netw. Appl. 1–10 (2019)
Wang, P., Huang, C.: An efficient fourth-order in space difference scheme for the nonlinear fractional Ginzburg-Landau equation. BIT 58, 783–805 (2018)
Zhang, K., Chen, L., An, Y., et al.: A QoE test system for vehicular voice cloud services. Mobile Netw. Appl. (2019). https://doi.org/10.1007/s11036-019-01415-3
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhang, L., Chen, L., Song, X. (2021). Preconditioned Iteration Method for the Nonlinear Space Fractional Complex Ginzburg-Landau Equation. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-030-72795-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-72795-6_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72794-9
Online ISBN: 978-3-030-72795-6
eBook Packages: Computer ScienceComputer Science (R0)