Sparse Implicit Projection (SIP) for reduction of general many-terminal networks

This paper is concerned with model order reduction of large scale dynamic systems that have sparse matrix representations, particularly systems with large numbers of input/output ldquoports.rdquo We present an algorithm that combines the advantages of widely-used approaches such as PRIMA and TICER but avoids many of the drawbacks of both. The resulting algorithm is capable of high-order rational approximation, exploits network sparsity, preserves passivity, can be extended to general non-symmetric systems, and can be applied to networks with hundreds or thousands of ports. We develop a common mathematical framework that can encompass all three algorithms, show mathematical relations between them, and point out certain special cases where they are equivalent. We show examples from analysis of industrial on-chip RC/RLC networks that demonstrate performance advantages of more than three orders of magnitude.