Selecting the optimal Parameters Results in Double Interpolation: Double AFD
Tao Qian, Yunni Wu, Wei Qu, Yanbo Wang

TL;DR
This paper introduces double AFD, a new sparse representation method that optimally selects parameters for double interpolation, including function and derivative values, outperforming classical AFD in Hardy spaces.
Contribution
It develops a double interpolation framework with optimal parameter selection, establishing convergence, approximation, and boundary interpolation results, and extends to higher-order derivatives and upper-half plane.
Findings
Double AFD outperforms classical AFD in sparse representation.
Optimal parameter selection leads to double interpolation of functions and derivatives.
Convergence and approximation properties are established for the new method.
Abstract
Let belong to the Hardy space of the unit disc, and the normalized Szeg\"o (reproducing) kernel of It is well known that, due to the reproducing kernel property, for any distinct points in the orthogonal projection of into denoted as interpolates at the points 's. The present study further proves that if the 's are optimally selected according to certain energy matching pursuit principle, then double interpolates at the points 's, or order interpolation, that is, \[ P_{{\rm span}\{e_{a_1},\cdots,e_{a_n}\}}(f)(a_k)=f(a_k), \quad {\rm and}\quad P_{{\rm span}\{e_{a_1},\cdots,e_{a_n}\}}'(f)(a_k)=f'(a_k),\quad k=1,\cdots,n.\] With the accordingly…
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