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arXiv · 1701.03042

A new shift strategy for the implicitly restarted generalized second-order Arnoldi method

FangHui Gong·Yuquan Sun
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Abstract

In this paper, a new shift strategy for the implicitly restarted generalized second-order Arnoldi (GSOAR) method is proposed. In implicitly restarted processes, we can get a $k$-step GSOAR decomposition from a $m$-step GSOAR decomposition by performing $p = m-k$ implicit shifted QR iterations. The problem of the implicitly restarted GSOAR is the mismatch between the number of shifts and the dimension of the subspace. There are $2p$ shifts for $p$ QR iterations. We use the shifts to filter out the unwanted information in the current subspace; when more shifts are used, one obtains a better updated subspace. But, if we use more than $p$ shifts, the structure of the GSOAR decomposition will be destroyed. We propose a novel method which can use all $2p$ candidates and preserve the special structure. The new method vastly enhances the overall efficiency of the algorithm. Numerical experiments illustrate the efficiency of every restart process.

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FangHui Gong, Yuquan Sun. 2017-01-11. A new shift strategy for the implicitly restarted generalized second-order Arnoldi method. https://arxiv.org/abs/1701.03042

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