Fast Polar Decomposition of an Arbitrary Matrix
نویسندگان
چکیده
The polar decomposition of an m x n matrix A of full rank, where rn n, can be computed using a quadratically convergent algorithm of Higham SIAMJ. Sci. Statist. Comput., 7 (1986), pp. 1160-1174]. The algorithm is based on a Newton iteration involving a matrix inverse. It is shown how, with the use of a preliminary complete orthogonal decomposition, the algorithm can be extended to arbitrary A. The use of the algorithm to compute the positive semidefinite square root of a Hermitian positive semidefinite matrix is also described. A hybrid algorithm that adaptively switches from the matrix inversion based iteration to a matrix multiplication based iteration due to Kovarik, and to Bj6rck and Bowie, is formulated. The decision when to switch is made using a condition estimator. This "matrix multiplication rich" algorithm is shown to be more efficient on machines for which matrix multiplication can be executed 1.5 times faster than matrix inversion. Key words, polar decomposition, complete orthogonal decomposition, matrix square root, matrix multiplication, Schulz iteration, condition estimator AMS(MOS) subject classification. 65F05 C.R. classification. G.1.3
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ورودعنوان ژورنال:
- SIAM J. Scientific Computing
دوره 11 شماره
صفحات -
تاریخ انتشار 1990