A Dual ADMM Framework for Inverse Parameter Identification in Elliptic Variational Inequalities
This paper is concerned with an inverse parameter identification problem governed by a second-kind elliptic variational inequality involving a non-differentiable boundary functional. The objective is to reconstruct an unknown Robin-type coefficient from boundary measurements while preserving the intrinsic nonsmooth structure of the underlying model. The inverse problem is formulated as a constrained optimization problem, where the cost functional measures the discrepancy between the computed and observed data and is stabilized by Tikhonov regularization. To overcome the difficulties associated with the nonsmooth variational inequality, we reformulate the state problem as a constrained minimization problem and employ an augmented Lagrangian framework combined with the alternating direction method of multipliers (ADMM). Fenchel duality is used to treat the non-differentiable boundary term without introducing smoothing approximations. We establish identifiability of the unknown coefficient and prove existence and convergence results for the proposed approximation scheme. Numerical experiments performed on a classical benchmark problem confirm the theoretical analysis and demonstrate that accurate parameter reconstruction can be achieved with reduced computational effort. The proposed methodology provides a robust framework for inverse problems arising in applications such as heat transfer, frictional contact, and corrosion detection.