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We consider infinite horizon optimal control problems with time averaging and time discounting criteria and give estimates for the Cesàro and Abel limits of their optimal values in the case when they depend on the initial conditions. We establish that these limits are bounded from above by the optimal value of a certain infinite dimensional (ID) linear programming (LP) problem and that they are bounded from below by the optimal value of the corresponding dual problem. (These estimates imply, in particular, that the Cesàro and Abel limits exist and are equal to each other if there is no duality gap). In addition, we obtain IDLP-based optimality conditions for the long run average optimal control problem, and we illustrate these conditions by an example.

We formulate and study the infinite dimensional linear programming (LP) problem associated with the deterministic discrete time long-run average criterion optimal control problem. Along with its du...

This paper is devoted to the study of infinite horizon optimal control problems with time discounting and time averaging criteria. These problems are related to certain infinite-dimensional linear programming (IDLP) problems, and this relation is used to obtain properties of the optimal value functions. We focus on the general non-ergodic case, where the optimal value functions may depend on the initial condition of the system. We also obtain IDLP-based sufficient and necessary optimality conditions for optimal control problems.

We formulate and study the infinite-dimensional linear programming problem associated with the deterministic long-run average cost control problem. Along with its dual, it allows one to characterize the optimal value of this control problem. The novelty of our approach is that we focus on the general case wherein the optimal value may depend on the initial condition of the system.

It has been recently established that a deterministic infinite horizon discounted optimal control problem in discrete time is closely related to a certain infinite dimensional linear programming problem and its dual. In the present paper, we use these results to establish necessary and sufficient optimality conditions for this optimal control problem and apply them to construct a near optimal control., 25 pages

This paper is devoted to a study of infinite horizon optimal control problems with time discounting and time averaging criteria in discrete time. We establish that these problems are related to certain infinite-dimensional linear programming (IDLP) problems. We also establish asymptotic relationships between the optimal values of problems with time discounting and long-run average criteria.

A problem of minimization of L1-penalized conditional value-at-risk (CVaR) is considered. It is shown that there exists a non-negative threshold value of the penalty parameter such that the optimal value of the penalized problem is unbounded if the penalty parameter is less than the threshold value, and it is bounded if the penalty parameter is greater or equal than this value. It is established that the threshold value can be found via the solution of a linear programming problem, and, therefore, readily computable. Theoretical results are illustrated by numerical examples. Refereed/Peer-reviewed

This paper is devoted to a study of a discrete time infinite horizon optimal control problem with time discounting criterion. We introduce an infinite-dimensional linear programming (IDLP) problem closely related to this problem. We derive necessary and sufficient conditions of optimality for the optimal control problem in terms of the solution of the dual to this IDLP problem.

It has been established recently that, under mild conditions, deterministic long run average problems of optimal control are “asymptotically equivalent” to infinite-dimensional linear programming problems (LPPs) and that these LPPs can be approximated by finite-dimensional LPPs. In this paper we introduce the corresponding infinite- and finite-dimensional dual problems and study duality relationships. We also investigate the possibility of using solutions of finite-dimensional LPPs and their duals for numerical construction of the optimal controls in periodic optimization problems. The construction is illustrated with a numerical example.

International audience; Linear programming formulations for the discounted and long-run average MDPs have evolved along separate trajectories. In 2006, E. Altman conjectured that the two linear programming formulations of discounted and long-run average MDPs are, most likely, a manifestation of general properties of singularly perturbed linear programs. In this note we demonstrate that this is, indeed, the case.

The paper aims at the development of tools for analysis and construction of near optimal solutions of singularly perturbed (SP) optimal controls problems with long run average optimality criteria. The idea that we exploit is to first asymptotically approximate a given problem of optimal control of the SP system by a certain averaged optimal control problem, then reformulate this averaged problem as an infinite-dimensional (ID) linear programming (LP) problem, and then approximate the latter by semi-infinite LP problems. We show that the optimal solution of these semi-infinite LP problems and their duals (that can be found with the help of a modification of an available LP software) allow one to construct near optimal controls of the SP system. We demonstrate the construction with a numerical example., Comment: 36 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:1309.3734

The paper aims at the development of an apparatus for analysis and construction of near optimal solutions of singularly perturbed (SP) optimal controls problems (that is, problems of optimal control of SP systems) considered on the infinite time horizon. We mostly focus on problems with time discounting criteria but a possibility of the extension of results to periodic optimization problems is discussed as well. Our consideration is based on earlier results on averaging of SP control systems and on linear programming formulations of optimal control problems. The idea that we exploit is to first asymptotically approximate a given problem of optimal control of the SP system by a certain averaged optimal control problem, then reformulate this averaged problem as an infinite-dimensional (ID) linear programming (LP) problem, and then approximate the latter by semi-infinite LP problems. We show that the optimal solution of these semi-infinite LP problems and their duals (that can be found with the help of a modification of an available LP software) allow one to construct near optimal controls of the SP system. We demonstrate the construction with two numerical examples., 53 pages, 10 figures

International audience; In this paper we study a linear programming problem with a linear perturbation introduced through a parameter $\epsilon > 0$. We identify and analyze an unusual asymptotic phenomenon in such a linear program. Namely, discontinuous limiting behavior of the optimal objective function value of such a linear program may occur even when the rank of the coefficient matrix of the constraints is unchanged by the perturbation. We show that, under mild conditions, this phenomenon is a result of the classical Slater constraint qualification being violated at the limit and propose an iterative, constraint augmentation approach for resolving this problem.

We investigate relationships between the deterministic infinite time horizon optimal control problem with discounting, in which the state trajectories remain in a given compact set Y, and a certain infinite dimensional linear programming (IDLP) problem. We introduce the problem dual with respect to this IDLP problem and obtain some duality results. We construct necessary and sufficient optimality conditions for the optimal control problem under consideration, and we give a characterization of the viability kernel of Y. We also indicate a way of how one can use finite dimensional approximations of the IDLP problem and its dual for construction of near optimal feedback controls.

Deterministic long run average optimal control problems and, in particular, periodic optimization problems are related to certain infinite-dimensional linear programming (LP) problems, which can be approximated by finite-dimensional LP problems. In this paper we study problems dual to these infinite- and finite- dimensional LP problems, and we investigate a possibility of using solutions of the latter for numerical construction of the optimal controls in periodic optimization problems. The construction is illustrated with a numerical example.

We investigate relationships between the deterministic infinite time horizon optimal control problem with discounting, in which the state trajectories remain in a given compact set Y, and a certain infinite dimensional linear programming (IDLP) problem. We introduce the problem dual with respect to this IDLP problem and obtain some duality results. We construct necessary and sufficient optimality conditions for the optimal control problem under consideration, and we give a characterization of the viability kernel of Y. We also indicate how one can use finite dimensional approximations of the IDLP problem and its dual for construction of near optimal feedback controls. The construction is illustrated with a numerical example. Refereed/Peer-reviewed

We present results establishing that deterministic long run average problems of optimal control are "asymptotically equivalent" to infinite dimensional linear programming problems (LPP) and that these LPP can be approximated by finite dimensional LPP, the solutions of which can be used for construction of the optimal controls. General results are illustrated by a numerical example.