Proximal algorithms for large-scale statistical modeling and optimal sensor/actuator selection
Proximal algorithms for large-scale statistical modeling and optimal sensor/actuator selection
复制标题
用于大规模统计建模和最佳传感器/执行器选择的近似算法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
T. Georgiou
中科院分区：
文献类型：
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作者：
A. Zare;Neil K. Dhingra;Mihailo R. Jovanovic;T. Georgiou
随机驱动动力系统的建模和控制中的几个问题可以转化为正则化半定程序。我们研究了两个这样的代表性问题，并表明它们可以用类似的方式表述。第一个是在统计建模中，试图通过适当且最小化地扰动先验动态来协调观察到的统计数据。第二，寻求最佳选择用于控制目的的传感器和执行器。为了解决大规模系统的建模和控制问题，我们使用近端方法开发了一个统一的算法框架。我们的定制算法利用问题结构，并允许处理统计建模以及传感器和执行器选择，其规模比当前通用求解器要大得多。
Several problems in modeling and control of stochastically-driven dynamical systems can be cast as regularized semi-definite programs. We examine two such representative problems and show that they can be formulated in a similar manner. The first, in statistical modeling, seeks to reconcile observed statistics by suitably and minimally perturbing prior dynamics. The second, seeks to optimally select sensors and actuators for control purposes. To address modeling and control of large-scale systems we develop a unified algorithmic framework using proximal methods. Our customized algorithms exploit problem structure and allow handling statistical modeling, as well as sensor and actuator selection, for substantially larger scales than what is amenable to current general-purpose solvers.