Distributed Unconstrained Optimization with Time-varying Cost Functions
This addresses optimization in dynamic multi-agent systems, but it is incremental as it builds on existing distributed optimization frameworks with time-varying extensions.
The paper tackles distributed unconstrained optimization with time-varying cost functions across networked agents, proposing a two-stage method that tracks the optimal trajectory by estimating descent directions and using consensus, resulting in an asymptotically reached upper bound on the gradient characterized by local cost properties.
In this paper, we propose a novel solution for the distributed unconstrained optimization problem where the total cost is the summation of time-varying local cost functions of a group networked agents. The objective is to track the optimal trajectory that minimizes the total cost at each time instant. Our approach consists of a two-stage dynamics, where the first one samples the first and second derivatives of the local costs periodically to construct an estimate of the descent direction towards the optimal trajectory, and the second one uses this estimate and a consensus term to drive local states towards the time-varying solution while reaching consensus. The first part is carried out by the implementation of a weighted average consensus algorithm in the discrete-time framework and the second part is performed with a continuous-time dynamics. Using the Lyapunov stability analysis, an upper bound on the gradient of the total cost is obtained which is asymptotically reached. This bound is characterized by the properties of the local costs. To demonstrate the performance of the proposed method, a numerical example is conducted that studies tuning the algorithm's parameters and their effects on the convergence of local states to the optimal trajectory.