Learning of Linear Dynamical Systems as a Noncommutative Polynomial Optimization Problem

• 成果类型: Article
• 署名作者: Zhou, Quan; Marecek, Jakub
• 署名单位: Imperial College London; Czech Technical University Prague
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3313351
• 发表日期: 2024
• 页码: 2399-2405
• 关键词: estimation; Filtering; optimization; POLYNOMIALS; System identification

摘要

There has been much recent progress in time series forecasting and estimation of system matrices of linear dynamical systems. We present an approach to both problems based on an asymptotically convergent hierarchy of convexifications of a certain nonconvex operator-valued problem, which is known as noncommutative polynomial optimization problem. We present promising computational results, including a comparison with methods implemented in MATLAB System Identification Toolbox.