Analytical Solutions to Minimum-Norm Problems

Abstract

For G∈Rm×n and g∈Rm, the minimization min∥Gψ−g∥2, with ψ∈Rn, is known as the Tykhonov regularization. We transport the Tykhonov regularization to an infinite-dimensional setting, that is min∥T(h)−k∥, where T:H→K is a continuous linear operator between Hilbert spaces H,K and h∈H,k∈K. In order to avoid an unbounded set of solutions for the Tykhonov regularization, we transform the infinite-dimensional Tykhonov regularization into a multiobjective optimization problem: min∥T(h)−k∥andmin∥h∥. We call it bounded Tykhonov regularization. A Pareto-optimal solution of the bounded Tykhonov regularization is found. Finally, the bounded Tykhonov regularization is modified to introduce the precise Tykhonov regularization: min∥T(h)−k∥with∥h∥=α. The precise Tykhonov regularization is also optimally solved. All of these mathematical solutions are optimal for the design of Magnetic Resonance Imaging (MRI) coils.

Design of Transcranial Magnetic Stimulation Coils With Optimized Stimulation Depth

“…Furthermore, all coil design problems in this work can be formulated as the optimisation in equation 7, which can be tackled by using supporting vector analysis, a concept derived from the Theory of Banach Spaces and Operator Theory which has been efficiently applied to obtain truly optimal solutions to minimisation and maximisation problems [19], [28].…”