论文标题
学习线性动力学系统作为非共同多项式优化问题
Learning of Linear Dynamical Systems as a Non-Commutative Polynomial Optimization Problem
论文作者
论文摘要
在预测下一个线性动力系统（LDS）的观察方面，最近有很多进展，这被称为不当学习以及对其系统矩阵的估计，这被称为LDS的正确学习。我们提出了一种适当学习LD的方法，尽管问题的非跨性别，但仍保证了数值解决方案与最小二乘估计量的全球收敛性。我们提出了有希望的计算结果。
There has been much recent progress in forecasting the next observation of a linear dynamical system (LDS), which is known as the improper learning, as well as in the estimation of its system matrices, which is known as the proper learning of LDS. We present an approach to proper learning of LDS, which in spite of the non-convexity of the problem, guarantees global convergence of numerical solutions to a least-squares estimator. We present promising computational results.
