Optimization with Zeroth-Order Oracles in Formation
Michael Elad, Daniel Zelazo, Tony A. Wood, Chris G. Manzie, Iman Shames · arXiv (Cornell University) · 2020
In this paper, we consider the optimisation of time varying functions by a network of agents with no gradient information. The proposed a novel method to estimate the gradient at each agent's position using only neighbour information. The gradient estimation is coupled with a formation controller, to minimise gradient estimation error and prevent agent collisions. Convergence results for the algorithm are provided for functions which satisfy the Polyak-Lojasiewicz inequality. Simulations and numerical results are provided to support the theoretical results.