Passive approximation and optimization using B-splines
Yevhen Ivanenko, Mats Gustafsson, B. L. G. Jonsson, Annemarie Luger,, B\"orje Nilsson, Sven Nordebo, Joachim Toft

TL;DR
This paper develops a method for passive approximation of complex functions using B-splines, formulating it as a convex optimization problem, and demonstrates its application in optimizing metamaterial-based linear systems.
Contribution
It introduces a B-spline based convex optimization approach for passive approximation of Herglotz functions, with a proof of density and practical application to metamaterials.
Findings
Finite B-spline expansions densely approximate Herglotz functions.
The approximation problem reduces to a finite-dimensional convex optimization.
Application example involves passive optimization of a metamaterial linear system.
Abstract
A passive approximation problem is formulated where the target function is an arbitrary complex valued continuous function defined on an approximation domain consisting of a finite union of closed and bounded intervals on the real axis. The norm used is a weighted -norm where . The approximating functions are Herglotz functions generated by a measure with H\"{o}lder continuous density in an arbitrary neighborhood of the approximation domain. Hence, the imaginary and the real parts of the approximating functions are H\"{o}lder continuous functions given by the density of the measure and its Hilbert transform, respectively. In practice, it is useful to employ finite B-spline expansions to represent the generating measure. The corresponding approximation problem can then be posed as a finite-dimensional convex optimization problem which is amenable for…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
