Stochastic recursive optimal control problem with monotonicity conditions under G-framework

[2508.17731] Stochastic recursive optimal control problem with monotonicity conditions under G-framework Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Mathematics > Optimization and Control arXiv:2508.17731 (math) [Submitted on 25 Aug 2025 ( v1 ), last revised 15 Aug 2026 (this version, v5)] Title: Stochastic recursive optimal control problem with monotonicity conditions under G-framework Authors: Qiangjun Tang View a PDF of the paper titled Stochastic recursive optimal control problem with monotonicity conditions under G-framework, by Qiangjun Tang View PDF HTML (experimental) Abstract: In this paper, we study the stochastic recursive optimal control problem under non-Lipschitz settings. More precisely, we suppose that the generator of G-BSDE describing the running cost is uniformly continuous and monotonic with respect to the first unknown variable. Using the comparison theorem for G-BSDE and the stability of viscosity solutions, we establish the dynamic programming principle and the connection between the value function and the viscosity solution of the associated Hamilton-Jacobi-Bellman equation. We provide an example of continuous time Epstein-Zin utility to demonstrate the application of our study. Subjects: Optimization and Control (math.OC) ; Probability (math.PR) MSC classes: 60G65, 60H10, 93E20, 49L25 Cite as: arXiv:2508.17731 [math.OC] (or arXiv:2508.17731v5 [math.OC] for this version) https://doi.org/10.48550/arXiv.2508.17731 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Qiangjun Tang [ view email ] [v1] Mon, 25 Aug 2025 07:19:39 UTC (36 KB) [v2] Wed, 3 Dec 2025 06:41:09 UTC (36 KB) [v3] Fri, 10 Apr 2026 01:29:37 UTC (1 KB) (withdrawn) [v4] Mon, 13 Apr 2026 01:32:25 UTC (1 KB) (withdrawn) [v5] Sat, 15 Aug 2026 00:54:12 UTC (30 KB) Full-text links: Access Paper: View a PDF of the paper titled Stochastic recursive optimal control problem with monotonicity conditions under G-framework, by Qiangjun Tang View PDF HTML (experimental) TeX Source view license Current browse context: math.OC < prev | next > new | recent | 2025-08 Change to browse by: math math.PR 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

Related documents

• Rough Stochastic Pontryagin Maximum Principle and an Indirect Shooting Method#1050742
• Hyperedge approximation for stochastic processes on higher-order networks.#449989
• Stochastic Optimal Control Laws for Cellular Artificial Muscles#325104
• Optimization methods for reinforcement learning: theory and applications#211648
• Bayesian reinforcement learning with Dirichlet processes#291862