Operator Newton Method for Large-Scale Coupled Riccati Equations Arising from Jump Systems

Consider a class of coupled discrete-time Riccati equations arising from jump systems. To compute their solutions when systems reach a steady state, we propose an operator Newton method and correspondingly establish its quadratic convergence under suitable assumptions. The advantage of the proposed method lies in the fact that its subproblems are solved using the operator Smith method, which allows it to maintain quadratic convergence in both the inner and outer iterations. Moreover, it does not require the constant term matrix of the equation to be invertible, making it more broadly applicable than existing inverse-free iterative methods. For large-scale problems, we develop a low-rank variant by incorporating truncation and compression techniques into the operator Newton framework. A complexity analysis is also provided to assess its scalability. Numerical experiments demonstrate that the presented low-rank operator Newton method is highly effective in approximating solutions to large-scale structured coupled Riccati equations.

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Journal
Axioms
Published
2025-08-01
DOI
https://doi.org/10.3390/axioms14080601
Primary Topic
Model Reduction and Neural Networks
Type
article
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article

Operator Newton Method for Large-Scale Coupled Riccati Equations Arising from Jump Systems

Bo Yu, Ning Dong, Yiwen Liu
Axioms
Model Reduction and Neural Networks
article

Operator Newton Method for Large-Scale Coupled Riccati Equations Arising from Jump Systems

Bo Yu, Ning Dong, Yiwen Liu
article en

Abstract

Consider a class of coupled discrete-time Riccati equations arising from jump systems. To compute their solutions when systems reach a steady state, we propose an operator Newton method and correspondingly establish its quadratic convergence under suitable assumptions. The advantage of the proposed method lies in the fact that its subproblems are solved using the operator Smith method, which allows it to maintain quadratic convergence in both the inner and outer iterations. Moreover, it does not require the constant term matrix of the equation to be invertible, making it more broadly applicable than existing inverse-free iterative methods. For large-scale problems, we develop a low-rank variant by incorporating truncation and compression techniques into the operator Newton framework. A complexity analysis is also provided to assess its scalability. Numerical experiments demonstrate that the presented low-rank operator Newton method is highly effective in approximating solutions to large-scale structured coupled Riccati equations.

AxiomsVol. 14(8)
Hunan University of Technology (CN)
Openalex Percentile: Top 84%
Model Reduction and Neural Networks
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