Computing the Bidiagonal SVD Through an Associated Tridiagonal Eigenproblem
نویسندگان
چکیده
In this paper, we present an algorithm for the singular value decomposition (SVD) of a bidiagonal matrix by means of the eigenpairs of an associated symmetric tridiagonal matrix. The algorithm is particularly suited for the computation of a subset of singular values and corresponding vectors. We focus on a sequential implementation, discuss special cases and other issues. We use a large set of bidiagonal matrices to assess the accuracy of the implementation and to identify potential shortcomings. We show that the algorithm can be up to three orders of magnitude faster than existing algorithms, which are limited to the computation of a full SVD.
منابع مشابه
Lapack Working Note 166: Computing the Bidiagonal Svd Using Multiple Relatively Robust Representations
We describe the design and implementation of a new algorithm for computing the singular value decomposition of a real bidiagonal matrix. This algorithm uses ideas developed by Großer and Lang that extend Parlett’s and Dhillon’s MRRR algorithm for the tridiagonal symmetric eigenproblem. One key feature of our new implementation is, that k singular triplets can be computed using only O(nk) storag...
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تاریخ انتشار 2016