Publication Results



Code: 55/2021
Title: ANALYSIS OF A GREEDY RECONSTRUCTION ALGORITHM
Date: Tuesday 3rd August 2021
Author(s) : Buchwald, S.; Ciaramella, G.; Salomon, J.
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Abstract: A novel and detailed convergence analysis is presented for a greedy algorithm that was introduced in 2009 by Maday and Salomin for operator reconstruction problems in the field of quantum mechanics. This algorithm is based on an offline/online decomposition of the reconstruction process and on an ansatz for the unknown operator obtained by an a priori chosen set of linearly independent matrices. The presented convergence analysis focuses on linear-quadratic (optimization) problems governed by linear differential systems and reveals the strong dependence of the performance of the greedy algorithm on the observability properties of the system and on the ansatz of the basis elements. Moreover, the analysis allows us to use a precise (and in some sense optimal) choice of basis elements for the linear case and led to the introduction of a new and more robust optimized greedy reconstruction algorithm. This optimized approach also applies to nonlinear Hamiltonian reconstruction problems, and its efficiency is demonstrated by numerical experiments.

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SIAM Journal on Control and Optimization