  The Arnoldi-Tikhonov method is a well-established regularization technique
for solving large-scale ill-posed linear inverse problems. This method
leverages the Arnoldi decomposition to reduce computational complexity by
projecting the discretized problem into a lower-dimensional Krylov subspace, in
which it is solved. This paper explores the iterated Arnoldi-Tikhonov method,
conducting a comprehensive analysis that addresses all approximation errors.
Additionally, it introduces a novel strategy for choosing the regularization
parameter, leading to more accurate approximate solutions compared to the
standard Arnoldi-Tikhonov method. Moreover, the proposed method demonstrates
robustness with respect to the regularization parameter, as confirmed by the
numerical results.
