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

Quan Zhou; Jakub Marecek

doi:10.1109/tac.2023.3313351

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arXiv · 2002.01444

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

Quan Zhou·Jakub Marecek

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Abstract

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.

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BibTeX RIS

Quan Zhou, Jakub Marecek. 2020-02-04. Learning of Linear Dynamical Systems as a Non-Commutative Polynomial Optimization Problem. https://doi.org/10.1109/tac.2023.3313351

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