带跳跃的无限地平线均场线性二次优化控制问题及相关哈密顿系统

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED Applied Mathematics and Optimization Pub Date : 2024-06-04 DOI:10.1007/s00245-024-10148-z
Qingmeng Wei, Yaqi Xu, Zhiyong Yu
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引用次数: 0

摘要

在这项工作中,我们重点研究带跳跃的无限视界均场线性-二次随机控制问题。首先,研究了无限视界线性均场随机微分方程和带跳跃的后向随机微分方程,以支持控制问题的研究。通过引入一种适合于涉及均场项和跳跃的系统的所谓耗散条件,研究了其解过程的全局可整性。对于控制问题,我们通过变分法总结出了开环最优控制的充分必要条件。此外,我们还利用续集法研究了一种无限视界全耦合线性均场前向后向随机微分方程。这样的研究使得开环最优控制的表征更加直接和完整。
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Infinite Horizon Mean-Field Linear Quadratic Optimal Control Problems with Jumps and the Related Hamiltonian Systems

In this work, we focus on an infinite horizon mean-field linear-quadratic stochastic control problem with jumps. Firstly, the infinite horizon linear mean-field stochastic differential equations and backward stochastic differential equations with jumps are studied to support the research of the control problem. The global integrability properties of their solution processes are studied by introducing a kind of so-called dissipation conditions suitable for the systems involving the mean-field terms and jumps. For the control problem, we conclude a sufficient and necessary condition of open-loop optimal control by the variational approach. Besides, a kind of infinite horizon fully coupled linear mean-field forward-backward stochastic differential equations with jumps is studied by using the method of continuation. Such a research makes the characterization of the open-loop optimal controls more straightforward and complete.

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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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