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首页> 外文期刊>SIAM Journal on Matrix Analysis and Applications >Computing the polar decomposition and the matrix sign decomposition in matrix groups
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Computing the polar decomposition and the matrix sign decomposition in matrix groups

机译:计算矩阵组中的极分解和矩阵符号分解

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摘要

For any matrix automorphism group G associated with a bilinear or sesquilinear form, Mackey, Mackey, and Tisseur have recently shown that the matrix sign decomposition factors of A is an element of G also lie in G; moreover, the polar factors of A lie in G if the matrix of the underlying form is unitary. Groups satisfying the latter condition include the complex orthogonal, real and complex symplectic, and pseudo-orthogonal groups. This work is concerned with exploiting the structure of G when computing the polar and matrix sign decompositions of matrices in G. We give sufficient conditions for a matrix iteration to preserve the group structure and show that a family of globally convergent rational Pade-based iterations of Kenney and Laub satisfy these conditions. The well-known scaled Newton iteration for computing the unitary polar factor does not preserve group structure, but we show that the approach of the iterates to the group is precisely tethered to the approach to unitarity, and that this forces a different and exploitable structure in the iterates. A similar relation holds for the Newton iteration for the matrix sign function. We also prove that the number of iterations needed for convergence of the structure-preserving methods can be precisely predicted by running an associated scalar iteration. Numerical experiments are given to compare the cubically and quintically converging iterations with Newton's method and to test stopping criteria. The overall conclusion is that the structure-preserving iterations and the scaled Newton iteration are all of practical interest, and which iteration is to be preferred is problem-dependent.
机译:对于任何与双线性或半线性形式相关的矩阵自同构群G,Mackey,Mackey和Tisseur最近都表明A的矩阵符号分解因子也是G的元素;此外,如果基础形式的矩阵是unit,则A的极性因子位于G中。满足后一种条件的组包括复正交,实和复辛,伪正交组。这项工作与在计算G中矩阵的极坐标和矩阵符号分解时利用G的结构有关。我们为矩阵迭代提供了充足的条件以保留组结构,并证明了基于Pade的全局收敛有理族肯尼和劳布满足这些条件。用于计算the极数的众所周知的按比例缩放的牛顿迭代法并没有保留组结构,但是我们证明了对组进行迭代的方法正好束缚于统一性方法,并且这迫使采用不同的可利用结构。迭代。矩阵符号函数的牛顿迭代具有类似的关系。我们还证明,可以通过运行关联的标量迭代来精确预测结构保留方法收敛所需的迭代次数。进行了数值实验,以比较牛顿法的三次和五次收敛迭代,并测试了停止准则。总的结论是,保留结构的迭代和按比例缩放的牛顿迭代都具有实际意义,并且哪个迭代是优选的取决于问题。

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