Irregular obstacles and quasi-variational inequalities of stochastic impulse control
Joint queue-perturbed and weakly coupled power control for wireless backbone networks
Wireless Backbone Networks (WBNs) equipped with Multi-Radio Multi-Channel (MRMC) configurations do experience power control problems such as the inter-channel and co-channel interference, high energy consumption at multiple queues and unscalable network connectivity. Such network problems can be conveniently modelled using the theory of queue perturbation in the multiple queue systems and also as a weak coupling in a multiple channel wireless network. Consequently, this paper proposes a queue perturbation...
Large deviations principle by viscosity solutions: the case of diffusions with oblique Lipschitz reflections
We establish a Large Deviations Principle for diffusions with Lipschitz continuous oblique reflections on regular domains. The rate functional is given as the value function of a control problem and is proved to be good. The proof is based on a viscosity solution approach. The idea consists in interpreting the probabilities as the solutions to some PDEs, make the logarithmic transform, pass to the limit, and then identify the action functional as the solution of the limiting equation.
Le problème de Cauchy pour les équations de Hamilton-Jacobi-Bellman
L'équation de Zakai et le problème séparé du contrôle optimal stochastique
Limiting average cost control problems in a class of discrete-time stochastic systems
We consider a class of -valued stochastic control systems, with possibly unbounded costs. The systems evolve according to a discrete-time equation (t = 0,1,... ), for each fixed n = 0,1,..., where the are i.i.d. random vectors, and the Gₙ are given functions converging pointwise to some function as n → ∞. Under suitable hypotheses, our main results state the existence of stationary control policies that are expected average cost (EAC) optimal and sample path average cost (SPAC) optimal for...
Linear quadratic control. State space vs. polynomial equations
Malliavin method for optimal investment in financial markets with memory
We consider a financial market with memory effects in which wealth processes are driven by mean-field stochastic Volterra equations. In this financial market, the classical dynamic programming method can not be used to study the optimal investment problem, because the solution of mean-field stochastic Volterra equation is not a Markov process. In this paper, a new method through Malliavin calculus introduced in [1], can be used to obtain the optimal investment in a Volterra type financial market....
Markov decision processes with time-varying discount factors and random horizon
This paper is related to Markov Decision Processes. The optimal control problem is to minimize the expected total discounted cost, with a non-constant discount factor. The discount factor is time-varying and it could depend on the state and the action. Furthermore, it is considered that the horizon of the optimization problem is given by a discrete random variable, that is, a random horizon is assumed. Under general conditions on Markov control model, using the dynamic programming approach, an optimality...
Maximizing banking profit on a random time interval.
Maximum principle for forward-backward doubly stochastic control systems and applications
The maximum principle for optimal control problems of fully coupled forward-backward doubly stochastic differential equations (FBDSDEs in short) in the global form is obtained, under the assumptions that the diffusion coefficients do not contain the control variable, but the control domain need not to be convex. We apply our stochastic maximum principle (SMP in short) to investigate the optimal control problems of a class of stochastic partial differential equations (SPDEs in short). And as an example...
Maximum principle for forward-backward doubly stochastic control systems and applications*
The maximum principle for optimal control problems of fully coupled forward-backward doubly stochastic differential equations (FBDSDEs in short) in the global form is obtained, under the assumptions that the diffusion coefficients do not contain the control variable, but the control domain need not to be convex. We apply our stochastic maximum principle (SMP in short) to investigate the optimal control problems of a class of stochastic partial differential equations (SPDEs in short). And as an...
Maximum principle for optimal control of fully coupled forward-backward stochastic differential delayed equations
This paper deals with the optimal control problem in which the controlled system is described by a fully coupled anticipated forward-backward stochastic differential delayed equation. The maximum principle for this problem is obtained under the assumption that the diffusion coefficient does not contain the control variables and the control domain is not necessarily convex. Both the necessary and sufficient conditions of optimality are proved. As illustrating examples, two kinds of linear quadratic...
Maximum process problems in optimal control theory.
Mean-variance optimal local reinsurance contracts
Méthodes stochastiques de dualité
Minimax control of a linear system with multinomial disturbances
Minimax control of a multivariate time-continuous linear stochastic system
Minimax control of a stochastic system with disturbances belonging to the exponential family
Minimax LQG control
This paper presents an overview of some recent results concerning the emerging theory of minimax LQG control for uncertain systems with a relative entropy constraint uncertainty description. This is an important new robust control system design methodology providing minimax optimal performance in terms of a quadratic cost functional. The paper first considers some standard uncertainty descriptions to motivate the relative entropy constraint uncertainty description. The minimax LQG problem under...