On the acceleration of optimal regularization algorithms for linear ill-posed inverse problems

Accelerated regularization algorithms for ill-posed problems have received much attention from researchers of inverse problems since the 1980s. The current optimal theoretical results indicate that some regularization algorithms, e.g. the ν-method and the Nesterov method, are such that under conventional source conditions the optimal convergence rates can be obtained with approximately the square root of the iterations of those needed for the benchmark (i.e. the Landweber iteration). In this paper, we propose a new class of regularization algorithms with parameter n, called the Acceleration Regularization of order n (ARⁿ). Theoretically, we prove that, for an arbitrary number n> - 1 , ARⁿ can attach the optimal convergence rates with approximately the n+ 1 root of the iterations needed for the benchmark method. Moreover, unlike the existing accelerated regularization algorithms, ARⁿs have no saturation restriction. Some symplectic iterative regularizing algorithms are developed for the numerical realization of ARⁿ. Finally, numerical experiments with integral equations and inverse problems in partial differential equations demonstrate that, at least for n≤ 2 , the numerical behavior of ARⁿ matches our theoretical findings, also breaking the practical acceleration capability of all existing regularization algorithms.