Sparsified multilevel adaptive cross approximation

摘要

In this paper, a sparsified multilevel adaptive cross approximation (SMLACA) is proposed to improve the sparsified adaptive cross approximation (SPACA). Compared with the SPACA, the SMLACA can save the CPU time and memory requirement for large targets. Numerical results are presented to validate the SMLACA and demonstrate its merits.