An Efficient, Memory-Saving Approach for the Loewner Framework

Abstract

AbstractThe Loewner framework is one of the most successful data-driven model order reduction techniques. If N is the cardinality of a given data set, the so-called Loewner and shifted Loewner matrices $${\mathbb {L}}\in {\mathbb {C}}^{N\times N}$$ L ∈ C N × N and $${\mathbb {S}}\in {\mathbb {C}}^{N\times N}$$ S ∈ C N × N can be defined by solely relying on information encoded in the considered data set and they play a crucial role in the computation of the sought rational model approximation.In particular, the singular value decomposition of a linear combination of $${\mathbb {S}}$$ S and $${\mathbb {L}}$$ L provides the tools needed to construct accurate models which fulfill important approximation properties with respect to the original data set. However, for highly-sampled data sets, the dense nature of $${\mathbb {L}}$$ L and $${\mathbb {S}}$$ S leads to numerical difficulties, namely the failure to allocate these matrices in certain memory-limited environments or excessive computational costs. Even though they do not possess any sparsity pattern, the Loewner and shifted Loewner matrices are extremely structured and, in this paper, we show how to fully exploit their Cauchy-like structure to reduce the cost of computing accurate rational models while avoiding the explicit allocation of $${\mathbb {L}}$$ L and $${\mathbb {S}}$$ S . In particular, the use of the hierarchically semiseparable format allows us to remarkably lower both the computational cost and the memory requirements of the Loewner framework obtaining a novel scheme whose costs scale with $$N \log N$$ N log N .