A Unifying Primal-Dual Proximal Framework for Distributed Nonconvex Optimization

This paper introduces a Unifying Primal-Dual Proximal (UPP) framework that linearizes the augmented Lagrangian to unify various distributed nonconvex optimization methods, proving sublinear convergence to stationary points and linear convergence under the Polyak-Łojasiewicz condition, while demonstrating superior performance through specialized algorithms like UPP-MC and Chebyshev-accelerated UPP-SC-OPT.

Zichong Ou, Jie LuWed, 11 Ma🔢 math

One-Way Thermo-Mechanical Coupled System Identification Using Displacement and Temperature Measurements

This paper presents an optimization-driven, adjoint-based framework that utilizes both monolithic and partitioned strategies to simultaneously identify structural damage and reconstruct temperature fields in one-way thermo-mechanically coupled systems, demonstrating superior accuracy over traditional assumptions even with sparse and suboptimally placed sensor networks.

Talhah Shamshad Ali Ansari, Suneth Warnakulasuriya, Ihar Antonau, Harbir Antil, Rainald Löhner, Roland WüchnerWed, 11 Ma🔢 math