论文标题
随机子空间牛顿凸方法应用于数据驱动的传感器选择问题
Randomized Subspace Newton Convex Method Applied to Data-Driven Sensor Selection Problem
论文作者
论文摘要
提出了用于传感器选择问题的随机子空间牛顿凸方法。随机子空间牛顿算法直接应用于凸公式，并且选择了更新变量的一部分作为当前最佳传感器候选者的自定义方法。在融合的解决方案中，几乎相同的结果是通过原始和随机的space-newton凸方法获得的。如预期的那样，随机 - space-newton方法需要更多的计算步骤，而它们会减少计算时间的总量，因为一个步骤的计算时间通过随机更新变量的数量比例的立方大大缩短，从而大大缩短了变量。从传感器的质量和计算时间方面，自定义方法显示出优于直接实现的性能。
The randomized subspace Newton convex methods for the sensor selection problem are proposed. The randomized subspace Newton algorithm is straightforwardly applied to the convex formulation, and the customized method in which the part of the update variables are selected to be the present best sensor candidates is also considered. In the converged solution, almost the same results are obtained by original and randomized-subspace-Newton convex methods. As expected, the randomized-subspace-Newton methods require more computational steps while they reduce the total amount of the computational time because the computational time for one step is significantly reduced by the cubic of the ratio of numbers of randomly updating variables to all the variables. The customized method shows superior performance to the straightforward implementation in terms of the quality of sensors and the computational time.
