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.

翻译：近期在预测线性动力系统（LDS）下一观测值方面取得了诸多进展，这被称为非适切学习；同时在估计其系统矩阵方面也取得了进展，这被称为LDS的适切学习。我们提出了一种LDS适切学习的方法，尽管该问题具有非凸性，但该方法能保证数值解全局收敛至最小二乘估计量。我们给出了有希望的计算结果。