An Accelerated Primal Dual Algorithm with Backtracking for Decentralized Constrained Optimization
This paper proposes D-APDB, a distributed accelerated primal-dual algorithm with backtracking that achieves optimal convergence for decentralized constrained optimization over undirected networks without requiring prior knowledge of Lipschitz constants, making it the first method of its kind to handle private nonlinear constraints efficiently.