Journal of Scientific Computing

, Volume 62, Issue 1, pp 198–229

A Third Order Fast Sweeping Method with Linear Computational Complexity for Eikonal Equations


DOI: 10.1007/s10915-014-9856-7

Cite this article as:
Wu, L. & Zhang, YT. J Sci Comput (2015) 62: 198. doi:10.1007/s10915-014-9856-7


Fast sweeping methods are a class of efficient iterative methods for solving steady state hyperbolic PDEs. They utilize the Gauss-Seidel iterations and alternating sweeping strategy to cover a family of characteristics of the hyperbolic PDEs in a certain direction simultaneously in each sweeping order. The first order fast sweeping method for solving Eikonal equations (Zhao in Math Comput 74:603–627, 2005) has linear computational complexity, namely, the computational cost is \(O(N)\) where \(N\) is the number of grid points of the computational mesh. Recently, a second order fast sweeping method with linear computational complexity was developed in Zhang et al. (SIAM J Sci Comput 33:1873–1896, 2011). The method is based on a discontinuous Galerkin (DG) finite element solver and causality indicators which guide the information flow directions of the nonlinear Eikonal equations. How to extend the method to higher order accuracy is still an open problem, due to the difficulties of solving much more complicated local nonlinear systems and calculations of local causality information. In this paper, we extend previous work and develop a third order fast sweeping method with linear computational complexity for solving Eikonal equations. A novel approach is designed for capturing the causality information in the third order DG local solver. Numerical experiments show that the method has third order accuracy and a linear computational complexity.


Fast sweeping methods Discontinuous Galerkin methods  High order accuracy Linear computational complexity Static Hamilton–Jacobi equations  Eikonal equations 

Mathematics Subject Classification


Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Applied and Computational Mathematics and StatisticsUniversity of Notre DameNotre DameUSA

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