Acceleration of Iterative Methods for Solving Linear Inverse Problems Based on Low-Rank Approximation

An efficient method is proposed for constructing a preconditioner for accelerated solution of systems of linear algebraic equations arising in solving linear inverse problems. The method relies on the properties of a low-rank approximation of the original system matrix and allows for a significant reduction in the number of iterations in iterative solution methods. Significant savings in computational resources can be achieved in the inverse problem of processing experimental data measured in a spatial domain separated from the domain of localization of the quantities to be reconstructed.