Communications in Information and Systems
Volume 20 (2020)
Mathematical Engineering: A special issue at the occasion of the 85th birthday of Prof. Thomas Kailath
Guest Editors: Ali H. Sayed, Helmut Bölcskei, Patrick Dewilde, Vwani Roychowdhury, and Stephen Shing-Toung Yau
Least squares optimal realisation of autonomous LTI systems is an eigenvalue problem
Pages: 163 – 207
We outline the solution of a long-standing open problem in system identification, on how to find the best least squares realisation of an autonomous linear time-invariant (LTI) dynamical system from given data. The global optimum is found among all stationary points of a least squares objective function, which we show to correspond to the eigen-tuples of a multi-parameter eigenvalue problem (MEVP). Such an MEVP can be solved by applying Forward (multi-) Shift Recursions to the given set of multivariate polynomial equations, generating so-called block Macaulay matrices, the null space of which can be modelled as the observability matrix of a multi-dimensional shift-invariant linear commutative singular system. The state equations of this system can be found from multi-dimensional realisation theory. From the corresponding eigen-tuples, one can then find the optimal parameters of the best LTI autonomous model. Our solution methodology uses ingredients from algebraic geometry, operator theory, multi-dimensional system theory and numerical linear algebra, and ultimately requires as basic building blocks only the singular value decomposition and eigen-solvers.
Surprisingly enough, the conclusion is that the globally optimal model in 1D least squares realisation, can be found exactly from multi-dimensional realisation. In addition, we describe several new, previously unknown, properties that characterise the optimal model and its behaviour.
This work was supported by KU Leuven: Research Fund (projects C16/15/ 059, C32/16/013, C24/18/022), Industrial Research Fund (Fellowship 13-0260) and several Leuven Research and Development bilateral industrial projects; Flemish Government Agencies: FWO (EOS Project no 30468160 (SeLMA), SBO project I013218N, PhD Grants (SB/1SA1319N, SB/1S93918, SB/151622)), EWI (PhD and postdoc grants Flanders AI Impulse Program), VLAIO (City of Things (COT.2018.018), PhD grants: Baekeland (HBC.20192204) and Innovation mandate (HBC.2019.2209), Industrial Projects (HBC.2018.0405)); European Commission (EU H2020-SC1-2016-2017 Grant No. 727721: MIDAS), the European Research Council (ERC) (EU Horizon 2020 Advanced Grant Agreement No. 885682).
Bart De Moor is a Fellow of IEEE and SIAM.
Received 27 February 2020
Published 19 November 2020