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Preprint / Version 5

Infinite Horizon Linear Optimal Control with Linear Constraints

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DOI:

https://doi.org/10.31224/5924

Keywords:

infinite horizon linear programming, linear optimal control, linear constraints, duality, infinite horizon dual linear programming problem

Abstract

We define infinite horizon linear optimal control problem with linear constraints. We provide a necessary condition for an optimal trajectory in terms of an infinite sequence of linear programming problems. We also provide a similar sufficient condition for optimality in terms of a related infinite sequence of linear programming problems. We define a “bang-bang sequence of decision rules”, and provide sufficient conditions for the existence of a unique optimal trajectory that is generated by such a sequence of decision rules. We also provide a “robust” approximation result in terms of a linear programming problem with a sufficiently long time horizon. We prove that the optimal value of the duals of the linear programming problems with “free end-point” used in the approximation result converge to the optimal value of the linear optimal control problem with linear constraints beginning from period 1 if and only if a weak transversality condition is satisfied. Under suitable assumptions we prove that there is a infinite horizon “implied dual linear programming problem” which has a solution and which along with the optimal trajectory satisfies the complementary slackness conditions. Further, the optimal value of the implied dual linear programming problem is equal to the optimal value of the maximization problem that gives rise to it. We obtain sufficient conditions for a trajectory to be an optimal trajectory by using the strong duality theorem and complementary slackness condition of linear programming.

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Author Biography

Somdeb Lahiri, (Formerly) PD Energy University (EU-G)

I retired on superannuation as Professor of Economics from PD Energy University (PDEU) on June 5, 2022.

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Posted

2025-12-03 — Updated on 2026-01-13

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Section 5 has been revised.