Learning of Linear Dynamical Systems as a Non-Commutative Polynomial Optimization Problem

Understand

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.