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Article Dans Une Revue SIAM Journal on Optimization Année : 2021

Accelerated Iterative Regularization via Dual Diagonal Descent

Résumé

We propose and analyze an accelerated iterative dual diagonal descent algorithm for the solution of linear inverse problems with strongly convex regularization and general data-fit functions. We develop an inertial approach of which we analyze both convergence and stability properties. Using tools from inexact proximal calculus, we prove early stopping results with optimal convergence rates for additive data terms and further consider more general cases, such as the Kullback--Leibler divergence, for which different type of proximal point approximations hold.
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Dates et versions

hal-03195616 , version 1 (29-11-2021)

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Luca Calatroni, Guillaume Garrigos, Lorenzo Rosasco, Silvia Villa. Accelerated Iterative Regularization via Dual Diagonal Descent. SIAM Journal on Optimization, 2021, 31 (1), pp.754-784. ⟨10.1137/19M1308888⟩. ⟨hal-03195616⟩
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