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Research Article | Open Access

A stochastic linear-quadratic optimal control problem with jumps in an infinite horizon

Jiali Wu1Maoning Tang2Qingxin Meng2( )
Department of Mathematics, Zhejiang Normal University, Jinhua 321004, China
Department of Mathematical Sciences, Huzhou University, Zhejiang 313000, China
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Abstract

In this paper, a stochastic linear-quadratic (LQ, for short) optimal control problem with jumps in an infinite horizon is studied, where the state system is a controlled linear stochastic differential equation containing affine term driven by a one-dimensional Brownian motion and a Poisson stochastic martingale measure, and the cost functional with respect to the state process and control process is quadratic and contains cross terms. Firstly, in order to ensure the well-posedness of our stochastic optimal control of infinite horizon with jumps, the L 2 -stabilizability of our control system with jump is introduced. Secondly, it is proved that the L 2 -stabilizability of our control system with jump is equivalent to the non-emptiness of the admissible control set for all initial state and is also equivalent to the existence of a positive solution to some integral algebraic Riccati equation (ARE, for short). Thirdly, the equivalence of the open-loop and closed-loop solvability of our infinite horizon optimal control problem with jumps is systematically studied. The corresponding equivalence is established by the existence of a s t a b i l i z i n g s o l u t i o n of the associated generalized algebraic Riccati equation, which is different from the finite horizon case. Moreover, any open-loop optimal control for the initial state x admiting a closed-loop representation is obatined.

CLC number: 60H10, 93E24

References

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AIMS Mathematics
Pages 4042-4078

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Cite this article:
Wu J, Tang M, Meng Q. A stochastic linear-quadratic optimal control problem with jumps in an infinite horizon. AIMS Mathematics, 2023, 8(2): 4042-4078. https://doi.org/10.3934/math.2023202

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Received: 19 June 2022
Revised: 19 October 2022
Accepted: 23 October 2022
Published: 15 February 2023
©2023 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)