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Seid Bahlali

Publications and source records attributed to Seid Bahlali.

6 recordsLinked to original sources
arXiv2008

A general necessary and sufficient optimality conditions for singular control problems

We consider a stochastic control problem where the set of strict (classical) controls is not necessarily convex and the the variable control has two components, the first being absolutely continuous and the second singular. The system is governed by a nonlinear stochastic differential equation, in which the absolutely continuous component of the control enters both the drift and the diffusion coefficients. By introducing a new approach, we establish necessary and sufficient optimality conditions for two models. The first concerns the relaxed-singular controls, who are a pair of processes whose first component is a measure-valued processes. The second is a particular case of the first and relates to strict-singular control problems. These results are given in the form of global stochastic maximum principle by using only the first order expansion and the associated adjoint equation. This improves and generalizes all the previous works on the maximum principle of controlled stochastic differential equations.

math.PR↗
arXiv2008

A general stochastic maximum principle for optimal control problems of forward-backward systems

Stochastic maximum principle of nonlinear controlled forward-backward systems, where the set of strict (classical) controls need not be convex and the diffusion coefficient depends explicitly on the variable control, is an open problem impossible to solve by the classical method of spike variation. In this paper, we introduce a new approach to solve this open problem and we establish necessary as well as sufficient conditions of optimality, in the form of global stochastic maximum principle, for two models. The first concerns the relaxed controls, who are a measure-valued processes. The second is a restriction of the first to strict control problems.

math.PR↗
arXiv2008

The strict and relaxed stochastic maximum principle for optimal control problem of backward systems

We consider a stochastic control problem where the set of controls is not necessarily convex and the system is governed by a nonlinear backward stochastic differential equation. We establish necessary as well as sufficient conditions of optimality for two models. The first concerns the strict (classical) controls. The second is an extension of the first to relaxed controls, who are a measure valued processes.

math.PR↗
arXiv2008

Necessary and sufficient optimality conditions for relaxed and strict control problems of backward systems

We consider a stochastic control problem where the set of strict (classical) controls is not necessarily convex, and the system is governed by a nonlinear backward stochastic differential equation. By introducing a new approach, we establish necessary as well as sufficient conditions of optimality for two models. The first concerns the relaxed controls, who are measure-valued processes. The second is a particular case of the first and relates to strict control problems.

math.OC↗
arXiv2008

A general stochastic maximum principle for mixed relaxed-singular control problems

We consider in this paper, mixed relaxed-singular stochastic control problems, where the control variable has two components, the first being measure-valued and the second singular. The control domain is not necessarily convex and the system is governed by a nonlinear stochastic differential equation, in which the measure-valued part of the control enters both the drift and the diffusion coefficients. We establish necessary optimality conditions, of the Pontryagin maximum principle type, satisfied by an optimal relaxed-singular control, which exist under general conditions on the coefficients. The proof is based on the strict singular stochastic maximum principle established by Bahlali-Mezerdi, Ekeland's variational principle and some stability properties of the trajectories and adjoint processes with respect to the control variable.

math.PR↗