Modified Iterations for Data-Sparse Solution of Linear Systems

Abstract

AbstractA modification of standard linear iterative methods for the solution of linear equations is investigated aiming at improved data-sparsity with respect to a rank function. The convergence speed of the modified method is compared to the rank growth of its iterates for certain model cases. The considered general setup is common in the data-sparse treatment of high dimensional problems such as sparse approximation and low rank tensor calculus.