Skip to Main content Skip to Navigation

Interior penalty approximation for optimal control problems. Optimality conditions in stochastic optimal control theory.

Abstract : This thesis is divided in two parts. In the first one we consider deterministic optimal control problems and we study interior approximations for two model problems with non-negativity constraints. The first model is a quadratic optimal control problem governed by a nonautonomous affine ordinary differential equation. We provide a first-order expansion for the penalized state an adjoint state (around the corresponding state and adjoint state of the original problem), for a general class of penalty functions. Our main argument relies on the following fact: if the optimal control satisfies strict complementarity conditions for its Hamiltonian, except for a set of times with null Lebesgue measure, the functional estimates of the penalized optimal control problem can be derived from the estimates of a related finite dimensional problem. Our results provide three types of measure to analyze the penalization technique: error estimates of the control, error estimates of the state and the adjoint state and also error estimates for the value function. The second model we study is the optimal control problem of a semilinear elliptic PDE with a Dirichlet boundary condition, where the control variable is distributed over the domain and is constrained to be non-negative. Following the same approach as in the first model, we consider an associated family of penalized problems, whose solutions define a central path converging to the solution of the original one. In this fashion, we are able to extend the results obtained in the ODE framework to the case of semilinear elliptic PDE constraints. In the second part of the thesis we consider stochastic optimal control problems. We begin withthe study of a stochastic linear quadratic problem with non-negativity control constraints and we extend the error estimates for the approximation by logarithmic penalization. The proof is based is the stochastic Pontryagin's principle and a duality argument. Next, we deal with a general stochastic optimal control problem with convex control constraints. Using the variational approach, we are able to obtain first and second-order expansions for the state and cost function, around a local minimum. This analysis allows us to prove general first order necessary condition and, under a geometrical assumption over the constraint set, second-order necessary conditions are also established.
Document type :
Complete list of metadata
Contributor : Francisco Silva Connect in order to contact the contributor
Submitted on : Thursday, December 2, 2010 - 11:28:14 AM
Last modification on : Saturday, June 25, 2022 - 7:41:37 PM
Long-term archiving on: : Thursday, March 3, 2011 - 2:46:56 AM


  • HAL Id : pastel-00542295, version 1


Francisco J. Silva. Interior penalty approximation for optimal control problems. Optimality conditions in stochastic optimal control theory.. Optimization and Control [math.OC]. Ecole Polytechnique X, 2010. English. ⟨pastel-00542295⟩



Record views


Files downloads