Seminario di analisi numerica
ore
11:00
The Löwner framework is one of the most successful data-driven model order reduction techniques.
Given k right interpolation data and h left interpolation data, the standard layout of this approach is composed of two stages.
First, the kh x kh Löwner matrix L and shifted Löwner matrix S are constructed. Then, an SVD of L-ζS, ζ belonging to one of the data sets, provides the projection matrices used to compute the sought reduced model.
These two steps become numerically challenging for large k and h in terms of both computational time and storage demand.
We show how the structure of L and S can be exploited to reduce the cost of performing (L-ζS)x while avoiding the explicit allocation of L and S.