Selective inversion of inductance matrix for large-scale sparse RLC simulation

The inverse of the inductance matrix (reluctance matrix) is amenable to sparsification to a much greater extent than the inductance matrix itself. However, the inversion and subsequent truncation of a large dense inductance matrix to obtain the sparse inverse is very time-consuming, and previously proposed window-based techniques cannot provide adequate accuracy. In this paper we propose a method for selective inversion of the inductance matrix to a prescribed sparsity ratio, which is also amenable to parallelization on modern architectures. Experimental results demonstrate its potential to provide efficient and accurate approximation of the reluctance matrix for simulation of large-scale RLC circuits.