A Separation Principle for Conditional Mean-Field Type Linear Quadratic Optimal Control Problem

[2512.17572] A Separation Principle for Conditional Mean-Field Type Linear Quadratic Optimal Control Problem Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Mathematics > Optimization and Control arXiv:2512.17572 (math) [Submitted on 19 Dec 2025 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: A Separation Principle for Conditional Mean-Field Type Linear Quadratic Optimal Control Problem Authors: Zhongbin Guo , Guangchen Wang View a PDF of the paper titled A Separation Principle for Conditional Mean-Field Type Linear Quadratic Optimal Control Problem, by Zhongbin Guo and Guangchen Wang View PDF HTML (experimental) Abstract: This paper investigates a conditional mean-field type linear quadratic (LQ) optimal control problem with partial observation and regime switching, where the conditional expectations of the state and control given the history of Markov chain enter into the dynamics and cost. The exact regime of Markov chain is accessible, whereas the system state can only be partially observed. A separation principle is established, showing that the estimate and control procedures can be separated and implemented independently. It extends the classical separation principle to conditional mean-field system. Utilizing two sets of Riccati equations and a set of first-order ordinary differential equations, we derive the feedback representation of the optimal control. To illustrate the effectiveness of the theoretical results, two applications with numerical simulations are provided, including a one-dimensional LQ example and a coupled electrical machines control problem. Subjects: Optimization and Control (math.OC) Cite as: arXiv:2512.17572 [math.OC] (or arXiv:2512.17572v2 [math.OC] for this version) https://doi.org/10.48550/arXiv.2512.17572 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhongbin Guo [ view email ] [v1] Fri, 19 Dec 2025 13:39:16 UTC (1,152 KB) [v2] Sat, 15 Aug 2026 08:12:05 UTC (4,403 KB) Full-text links: Access Paper: View a PDF of the paper titled A Separation Principle for Conditional Mean-Field Type Linear Quadratic Optimal Control Problem, by Zhongbin Guo and Guangchen Wang View PDF HTML (experimental) TeX Source view license Current browse context: math.OC < prev | next > new | recent | 2025-12 Change to browse by: math References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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