Error bound of the multilevel adaptive cross approximation (MLACA)

An error bound of the multilevel adaptive cross approximation (MLACA 1, which is a multilevel version of the adaptive cross approximation-singular value decomposition (ACA-SVD), is rigorously derived. For compressing an off-diagonal submatrix of the method of moments MAD impedance matrix with a binary tree, the L-level MIACA includes L + 1 steps, and each step includes 2(L) ACA-SVD decompositions. If the relative Frobenius norm error of the ACA-SVD used in the MLACA is smaller than epsilon, the rigorous proof in this communication shows that the relative Frobenius norm error of the L-Ievel MLACA is smaller than (1 + epsilon)(L+1) - 1. In practical applications, the error bound of the MLACA can be approximated as epsilon(L + 1), because epsilon is always << 1. The error upper bound can he used to control the accuracy of the MLACA. To ensure an error of the L-level MLACA smaller than epsilon for different L, the ACA-SVD threshold can be set to (1 + epsilon)1/L+1 - 1, which approximately equals epsilon/(L + 1) for practical applications.Peer ReviewedPostprint (author's final draft

Rotating non-Kerr black hole and energy extraction

The properties of the ergosphere and energy extraction by Penrose process in a rotating non-Kerr black hole are investigated. It is shown that the ergosphere is sensitive to the deformation parameter $\epsilon$ and the shape of the ergosphere becomes thick with increase of the parameter $\epsilon$. It is of interest to note that, comparing with the Kerr black hole, the deformation parameter $\epsilon$ can enhance the maximum efficiency of the energy extraction process greatly. Especially, for the case of $a>M$, the non-Kerr metric describes a superspinning compact object and the maximum efficiency can exceed 60%, while it is only 20.7% for the extremal Kerr black hole.Comment: 16 pages, 5 figures, and 2 table