This paper addresses the direct (noniterative) solution of the method-of-moments (MoM) linear system, accelerated through block-wise compression of the MoM impedance matrix. Efficient matrix block compression is achieved using the adaptive cross-approximation (ACA) algorithm and the truncated singular value decomposition (SVD) postcompression. Subsequently, a matrix decomposition is applied that preserves the compression and allows for fast solution by backsubstitution. Although not as fast as some iterative methods for very large problems, accelerated direct solution has several desirable features, including: few problem-dependent parameters; fixed time solution avoiding convergence problems; and high efficiency for multiple excitation problems [e.g., monostatic radar cross section (RCS)]. Emphasis in this paper is on the multiscale compressed block decomposition (MS-CBD) algorithm, introduced by Heldring , which is numerically compared to alternative fast direct methods. A new concise proof is given for theN2computational complexity of the MS-CBD. Some numerical results are presented, in particular, a monostatic RCS computation involving 1 043 577 unknowns and 1000 incident field directions, and an application of the MS-CBD to the volume integral equation (VIE) for inhomogeneous dielectrics.

A novel algorithm, the compressed block decomposition, is presented for highly accelerated direct (noniterative) method-of-moment capacitance extraction. The algorithm is based on a blockwise subdivision of the method-of-moment potential coefficient matrix. Matrix subblocks corresponding to distant subregions of the problem geometry are not calculated directly but approximated in a compressed form. Subsequently, the matrix is decomposed using an algorithm that preserves the compression. The efficiency of the method is demonstrated on a common benchmark problem - a 6 times 6 bus crossing. The numerical cost of the algorithm is shown, both theoretically and numerically, to scale with N log3N and the storage space with N log2N.