Learning of Linear Dynamical Systems as a Non-Commutative Polynomial Optimization Problem
Optimization and Control
2024-02-28 v6 Machine Learning
Systems and Control
Systems and Control
Machine Learning
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.
Cite
@article{arxiv.2002.01444,
title = {Learning of Linear Dynamical Systems as a Non-Commutative Polynomial Optimization Problem},
author = {Quan Zhou and Jakub Marecek},
journal= {arXiv preprint arXiv:2002.01444},
year = {2024}
}
Comments
14 pages, 4 figures; retitled to reflect the title of the the published version