Deterministic and Stochastic Optimal Control and Inverse Problems [Paperback]

1. All-At-Once Formulation Meets the Bayesian Approach: A Study of Two Prototypical Linear Inverse Problems 2. On Iterated Tikhonov Kaczmarz Type Methods for Solving Systems of Linear Ill-posed Operator Equations 3. On Numerical Approximation of Optimal Control for Stokes Hemivariational Inequalities 4. Nonlinear Tikhonov Regularization in Hilbert Scales with Oversmoothing Penalty: Inspecting Balancing Principles 5. An Optimization Approach to Parameter Identification in Variational Inequalities of Second Kind-II 6. Generalized Variational-hemivariational Inequalities in Fuzzy Environment 7. Boundary Stabilization of the Linear MGT Equation with Feedback Neumann Control 8. Sweeping Process Arguments in the Analysis and Control of a Contact Problem 9. Anderson Acceleration for Degenerate and Nondegenerate Problems 10. Approximate Coincidence Points for Single-valued Maps and Aubin Continuous Set-valued Maps 11. Stochastic Variational Approach for Random Cournot-Nash Principle 12. Augmented Lagrangian Methods For Optimal Control Problems Governed by Mixed Variational-Hemivariational Inequalities Involving a Set-valued Mapping 13. Data Driven Reconstruction Using Frames and Riesz Bases 14. Antenna Problem Induced Regularization and Sampling Strategies 15. An Equation Error Approach for Identifying a Random Parameter in a Stochastic Partial Differential Equation

Inverse problems of identifying parameters and initial/boundary conditions in deterministic and stochastic partial differential equations constitute a vibrant and emerging research area that has found numerous applications. A related problem of paramount importance is the optimal control problem for stochastic differential equations.

This edited volume comprises invited contributions from world-renowned researchers in the subject of control and inverse problems. There are several contributions on optimal control and inverse problems covering different aspects of the tlã: