A Semiorthogonal Generalized Arnoldi Method and Its Variations for Quadratic Eigenvalue Problems

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4 Comparison of the number of iterations and the number of matrix-vector products by BChebyDLR and a-BChebyDLR (which employs the adaptive strategy), using the same block size = 15

We show that an orthonormal Krylov basis for this class of matrices can be generated by a short recurrence relation based on GMRES residual vectors.. These residual vectors are

For the 48 small-sized instances of Set I, Table A1 and Figure 3(a) show that our MAGQMK algorithm is able to attain the best known results for 45 out of 48 (93.75%) cases including

TABLE A4: Comparative results of our MAGQMK algorithm on the 48 large-sized instances of Set II with respect to 3 state- of-the-art algorithms presented in reference [30] cited in

Based on QR~\ike décomposition with column pivoting, a new and efficient numerical method for solving symmetrie matrix inverse eigenvalue problems is proposed, which is suitable

In the numencal solution of elhptic boundary value problems, ît is well- known that the présence of corners m the domain can cause a loss of accuracy in the solution Many methods