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Journal Articles ESAIM: Control, Optimisation and Calculus of Variations Year : 2024

Dynamical programming for off-the-grid dynamic inverse problems

Abstract

In this work we consider off-the-grid algorithms for the reconstruction of sparse measures from time-varying data. In particular, the reconstruction is a finite collection of Dirac measures whose locations and masses vary continuously in time. Recent work showed that this decomposition was possible by minimising a convex variational model which combined a quadratic data fidelity with dynamical Optimal Transport. We generalise this framework and propose new numerical methods which leverage efficient classical algorithms for computing shortest paths on directed acyclic graphs. Our theoretical analysis confirms that these methods converge to globally optimal reconstructions. Numerically, we show new examples for unbalanced Optimal Transport penalties, and for balanced examples we are 100 times faster in comparison to the previously known method.
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Origin : Publication funded by an institution

Dates and versions

hal-04450197 , version 1 (21-12-2021)
hal-04450197 , version 2 (23-12-2022)
hal-04450197 , version 3 (09-02-2024)

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Cite

Vincent Duval, Robert Tovey. Dynamical programming for off-the-grid dynamic inverse problems. ESAIM: Control, Optimisation and Calculus of Variations, 2024, 30, pp.7. ⟨10.1051/cocv/2023085⟩. ⟨hal-04450197v3⟩
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