$$\mathcal {H}_2$$ optimal rational approximation on general domains

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED
Alessandro Borghi, Tobias Breiten
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引用次数: 0

Abstract

Optimal model reduction for large-scale linear dynamical systems is studied. In contrast to most existing works, the systems under consideration are not required to be stable, neither in discrete nor in continuous time. As a consequence, the underlying rational transfer functions are allowed to have poles in general domains in the complex plane. In particular, this covers the case of specific conservative partial differential equations such as the linear Schrödinger and the undamped linear wave equation with spectra on the imaginary axis. By an appropriate modification of the classical continuous time Hardy space \(\varvec{\mathcal {H}}_{\varvec{2}}\), a new \(\varvec{\mathcal {H}}_{\varvec{2}}\)-like optimal model reduction problem is introduced and first-order optimality conditions are derived. As in the classical \(\varvec{\mathcal {H}}_{\varvec{2}}\) case, these conditions exhibit a rational Hermite interpolation structure for which an iterative model reduction algorithm is proposed. Numerical examples demonstrate the effectiveness of the new method.

一般域上的 $$\mathcal {H}_2$$ 最佳理性逼近
研究了大规模线性动力系统的最优模型还原。与大多数现有著作不同的是,所考虑的系统既不要求离散时间稳定,也不要求连续时间稳定。因此,允许基本的有理传递函数在复平面的一般域中具有极点。这尤其涵盖了特定保守偏微分方程的情况,如线性薛定谔方程和无阻尼线性波方程的虚轴谱。通过对经典连续时间哈代空间 (\varvec{mathcal {H}}_{\varvec{2}}\ )的适当修改,引入了一个新的类(\varvec{mathcal {H}}_{\varvec{2}}\ )最优模型还原问题,并导出了一阶最优性条件。与经典的 \(\varvec{mathcal {H}}_{\varvec{2}}\) 情况一样,这些条件表现出一种合理的 Hermite 插值结构,为此提出了一种迭代模型还原算法。数值示例证明了新方法的有效性。
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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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