New iterative methods for generalized singular-value problems

This paper presents two new iterative methods to compute generalized singular values and vectors of a large sparse matrix. To reach acceleration in the convergence process, we have used a different inner product instead of the common one, Euclidean one. Furthermore, at each restart, a different inner product has been chosen by the researchers. A number of numerical experiments illustrate the performance of the above-mentioned methods.