Abstract

A large dense complex linear system is obtained when solving an electromagnetic scattering of arbitrary shaped object located above a lossy half-space with the surface integral equation approach. To analyze the large dense complex linear system efficiently, the multilevel QR (MLQR) is used to accelerate the matrix-vector multiplication when the corresponding matrix equation is solved by a Krylov-subspace iterative method. Although the MLQR is more efficient than the direct solution, this paper presents a novel recompression technique to further reduce computation time and storage memory. The technique applies the multilevel simply sparse method (MLSSM) to the matrices of MLQR. Using the MLSSM, a sparser representation of the impedance matrix is obtained, and a more efficient matrix-vector multiplication is implemented. The combined MLQR/MLSSM is comparable to the MLQR and the adaptive cross-approximation/singular value decomposition (ACA-SVD) in terms of computation time and memory requirement. Remarkably, the new formulation can reduce the computational time and memory by about one order of magnitude, with excellent accuracy.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.