Generalised gossip-based subgradient method for distributed optimisation

This paper presents a generalised time-synchronous gossip-based algorithm for solving distributed optimisation problems associated with multi-agent networked systems. The proposed algorithm presents a generalisation such that the optimisation process can operate in the entire spectrum from ‘complete consensus’ to ‘complete disagreement’. A user-defined control parameter θ is identified for controlling such tradeoff as well as the temporal convergence properties. We formulate the algorithm based upon generalised time-synchronous gossip algorithm and subgradient method and provide analytical results for first and second moment convergence analysis. The proposed algorithm also provides a convergence rate estimate of O(1/m) in the number of iterations m when the step size is constant. We consider the effect of noise in networked systems from the perspectives of modelling uncertainty and measurement noise in subgradient estimation process and communication among agents, respectively. A numerical case study based on a multi-agent building energy system is used to validate the proposed algorithm.