This edited volume comprises invited contributions from world-renowned researchers in the subject of stochastic control and inverse problems. There are several contributions on stochastic optimal control and stochastic inverse problems covering different aspects of the theory, numerical methods, and applications.
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 theory, numerical methods, and applications. Besides a unified presentation of the most recent and relevant developments, this volume also presents some survey articles to make the material self-contained. To maintain the highest level of scientific quality, all manuscripts have been thoroughly reviewed.
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
Baasansuren Jadamba is an Associate Professor at the Rochester Institute of Technology. She received her Ph.D. from Friedrich-Alexander University Erlangen-Nuremberg in 2004. Her research interests are numerical analysis of partial differential equations, finite element methods, parameter identification problems in partial differential equations, and stochastic equilibrium problems.
Akhtar A. Khan is a Professor at the Rochester Institute of Technology. His research deals with set-valued Optimization, inverse problems, and variational inequalities. He is a co-author of Set-valued Optimization, Springer (2015), and Co-editor of Nonlinear Analysis and Variational Problems, Springer (2009). He is Co-Editor in Chief of the Journal of Applied and Numerical Optimization, and Editorial Board member of Optimization, Journal of Optimization Theory and Applications, and Journal of Nonlinear and Variational Analysis.
Stanisaw Migórski received his Ph.D. and Habilitation from Jagiellonian University in Krakow (JUK). Currently, he is a Full and Chair Professor of Mathematics at the Faculty of Mathematics and Computer Science at JUK. He published research work in the field of mathematical analysis and applications (partial differential equations, variational inequalities, optimal control, and Optimization). He has edited and authored books from publishers Kluwer/Plenum, Springer and Chapman & Hall.
Miguel Sama is an associate professor at Universidad Nacional Educación a Distancia (Madrid, Spain). His research is broadly on Optimization, focusing mainly on Applied Mathematics Models. His research interests are in infinite-dimensional optimization problems. They cover a wide range of theoretical and applied topics such as Ordered Vector Spaces, Set-Valued Analysis, Vector, Set-Valued Optimization, PDE-constrained Optimization, Inverse Problems, Optimal Control Problems, and Uncertainty Quantification.
