An Improved Shrunken Primal-Dual Subgradient (i-SPDS) algorithm for Optimization in Large-Scale Networked Environment

Abstract

Large-scale optimization in networked environment is challenged by scalability. To address this challenge, this paper sets its aim on developing algorithms for optimization problems with strongly coupled objective function and global constraints. As a radical improvement of the state-of-the-art algorithms, this paper develops an improved shrunken-primal-dual subgradient (i-SPDS) algorithm that establishes fast convergence and eliminates optimality gap. The developed algorithm is rather generic in that it can be readily implemented in any applications that involve large-scale networked environment. Simulations are conducted to demonstrate the efficacy and efficiency of the proposed i-SPDS.