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Computation of approximate null vectors of Sylvester and Lyapunovoperators

机译:Sylvester和Lyapunov算子的近似零向量的计算

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This paper describes an effective algorithm for computing approximate null vectors of certain matrix operators associated with Sylvester or Lyapunov equations. The singular value decomposition and rank-revealing QR methods are two widely used stable algorithms for numerical determination of the rank and nullity of a matrix A. These methods, however, are not readily applicable to Sylvester and Lyapunov operators since they require on the order of n6 arithmetic operations on order n2 data. For these problems, a variant of inverse power iteration is employed to compute orthonormal bases for singular subspaces associated with the small singular values. The method is practical since it relies only on the ability to solve a Sylvester or Lyapunov equation. Certain practical aspects are considered, and a direct refinement technique is proposed to enhance the convergence of the algorithm
机译:本文介绍了一种有效的算法,用于计算与Sylvester或Lyapunov方程相关的某些矩阵算符的近似零向量。奇异值分解和秩揭示QR方法是用于数值确定矩阵A的秩和零值的两种广泛使用的稳定算法。但是,这些方法不适用于Sylvester和Lyapunov运算符,因为它们需要的阶数为对n2阶数据进行n6算术运算。对于这些问题,采用了逆幂迭代的变体来计算与小奇异值关联的奇异子空间的正交基。该方法是实用的,因为它仅取决于求解Sylvester或Lyapunov方程的能力。考虑了某些实际方面,并提出了一种直接优化技术以增强算法的收敛性

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