Interpolatory Methods for Generic BizJet Gust Load Alleviation Function

The paper defines the Nyquist-band discretization error e∞, chooses 2m frequencies, enforces R(iωk)Kd(e^{iωkh}) = K(iωk), builds a Loewner interpolant from the data {zk=e^{iωkh}, R(iωk)^{-1}K(iωk)}, compresses it to minimal order via SVD, and finally enforces stability by a Nehari projection P∞, noting the potential order drop by the multiplicity m of the largest unstable Hankel singular value and recommending pre-projection “slack” to hit an order cap (Algorithm 1) . The candidate solution mirrors this pipeline step-for-step (tangential Loewner data and descriptor realization; SVD-based minimal projection; Nehari projection with degree drop by m; add slack) and adds a simple a posteriori inequality e∞(K,Kd) ≤ e∞(K,Kr_d) + ||Kr_d − Kd||L∞ using |R(iω)| ≤ 1, which is consistent with the paper’s evaluation guidance (compute ||Kr_d−Kd||L∞ as an error proxy) . No substantive conflicts were found.