大型和稀疏宏模型的快速被动执行

Stefano Grivet-Talocia
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引用次数: 12

摘要

本文提出了一种快速测试和增强以大而稀疏结构为特征的线性宏观模型的无源性的技术。提出了一种计算相关哈密顿矩阵虚特征值的优化算法,该算法对相关哈密顿矩阵进行迭代摄动直至强制无源。该方案的每次迭代只需要很小的计算成本,并且只与问题的大小线性扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast passivity enforcement for large and sparse macromodels
This work presents a fast technique for testing and enforcing passivity of linear macromodels characterized by a large and sparse structure. An optimized algorithm is proposed for the computation of the imaginary eigenvalues of the associated Hamiltonian matrix, which are iteratively perturbed until passivity is enforced. Each iteration of the proposed scheme requires a small computational cost that scales only linearly with the size of the problem.
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