A Decentralized Step-size Technique Used to Solve Large Scale Constrained State Estimation Problem

In previous research, we have proposed a parallel block scale gradient (PBSG) method with decentralized step-size for block additive unconstrained optimization problems of large distributed system. In current paper, we propose a distributed dual-type (DDT) method which differs from the conventional Karush-Kuhn-Tucker (KKT) method by treating the inequality constraints as the domain of the primal variables in the dual function and using projection theory to handle the inequality constraints. This method imbedded with decentralized step-size technique can be used to solve large scale constrained state estimation problem. We present this algorithm, demonstrate its computational efficiency through numerical simulations and compare it with the conventional method