Lavrentiev-prox-regularization for optimal controlof PDEs with state constraints

AUTOR(ES)

Gugat, Martin

FONTE

Computational & Applied Mathematics

DATA DE PUBLICAÇÃO

2009

RESUMO

A Lavrentiev prox-regularization method for optimal control problems with point-wise state constraints is introduced where both the objective function and the constraints are regularized. The convergence of the controls generated by the iterative Lavrentiev prox-regularization algorithm is studied. For a sequence of regularization parameters that converges to zero, strong convergence of the generated control sequence to the optimal control is proved. Due to the proxcharacter of the proposed regularization, the feasibility of the iterates for a given parameter can be improved compared with the non-prox Lavrentiev-Regularization.

Documentos Relacionados

• Numerical solution of the variational PDEs arising in optimal control theory
• Informational constraints on optimal sex allocation in ants
• Intelligent controllers for dynamic systems with state and input constraints and subjected to model uncertainties.
• Initial values for Riccati ODEs from variational PDEs
• OPTIMAL DESIGN OF PLANAR FRAMES WITH STABILITY CONSTRAINTS.