Efficient Computation of the Maximum Eigenvalue of Large Symmetric Matrices
نویسنده
چکیده
Though the implicitly restarted Arnoldi/Lanczos method in ARPACK is a reliable method for computing a few eigenvalues of large-scale matrices, it can be inefficient because it only checks for convergence at restarts. Significant savings in runtime can be obtained by checking convergence at each Lanczos iteration. We describe a new convergence test for the maximum eigenvalue that is numerically stable, and is faster and uses an order of magnitude less memory than bisection and inverse iteration.
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