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arXiv 2311.08321math.OCcs.SYeess.SY
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使用凸优化对有理系统的峰值估计

Peak Estimation of Rational Systems using Convex Optimization

Jared Miller, Roy S. Smith

AI总结:

本文提出基于测度绝对连续性的有理求和法,将连续时间有理系统的非凸峰值估计转化为凸无限维线性规划,并用矩-SOS层次截断求解,相比现有方法具有更低计算复杂度和更高上界精度。

摘要:

本文提出了算法,用于对具有有理动力学的连续时间系统在轨迹上的状态函数峰值进行上界估计。有限维但非凸的峰值估计问题被转化为基于占据测度的凸无限维线性规划。随后,该无限维规划通过半定规划的矩-平方和(SOS)层次被截断为有限维。先前使用矩-SOS方法处理有理动力学的工作涉及将动力学通分,或添加提升变量以在新等式约束下处理倒数项。我们的求解方法采用了一种基于测度绝对连续性的有理求和法。与先前方法相比,我们程序的矩-SOS截断具有更低的计算复杂度,且在示例系统中(经实证证明)具有更高的上界精度。

英文摘要:

This paper presents algorithms that upper-bound the peak value of a state function along trajectories of a continuous-time system with rational dynamics. The finite-dimensional but nonconvex peak estimation problem is cast as a convex infinite-dimensional linear program in occupation measures. This infinite-dimensional program is then truncated into finite-dimensions using the moment-Sum-of-Squares (SOS) hierarchy of semidefinite programs. Prior work on treating rational dynamics using the moment-SOS approach involves clearing dynamics to common denominators or adding lifting variables to handle reciprocal terms under new equality constraints. Our solution method uses a sum-of-rational method based on absolute continuity of measures. The Moment-SOS truncations of our program possess lower computational complexity and (empirically demonstrated) higher accuracy of upper bounds on example systems as compared to prior approaches.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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