The distributed online algorithms which are based on the Frank-Wolfe method can effectively deal with constrained optimisation problems. However, the calculation of the full (sub)gradient vector in those algorithms le...
详细信息
The distributed online algorithms which are based on the Frank-Wolfe method can effectively deal with constrained optimisation problems. However, the calculation of the full (sub)gradient vector in those algorithms leads to a huge computational cost at each iteration. To reduce the computational cost of the algorithms mentioned above, the authors present a distributedonline randomised block-coordinate Frank-Wolfe algorithm over networks. Each agent in the networks only needs to calculate a subset of the coordinates of its (sub)gradient vector in this algorithm. Furthermore, they make a detailed theoretical analysis of the regret bound of this algorithm. When all local objective functions satisfy the conditions of strongly convex functions, the authors' algorithm attains the regret bound of O(root T), where T is the total number of iterations. Furthermore, the theorem results are verified via simulation experiments, which show that the algorithm is effective and efficient.
In this paper, we consider online multi-cluster games in which each cluster plays with each other to reach a Nash equilibrium, while all players in the cluster cooperate with each other to minimise their aggregate tim...
详细信息
ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
In this paper, we consider online multi-cluster games in which each cluster plays with each other to reach a Nash equilibrium, while all players in the cluster cooperate with each other to minimise their aggregate time-varying cost *** addition, the actions of the players are affected by local constraints and time-varying coupling constraints. We use a leader-follower based estimator and develop a distributedonline algorithm that seeks Nash equilibrium sequences for online multi-cluster games by using the projected gradient method and the primal-dual method. We show that the designed algorithm ensures that both dynamic regret and dynamic fit are sublinearly bounded. Finally, we illustrate the effectiveness of the algorithm through an electricity market game involving multiple microgrid clusters.
The MONEE framework endows collective adaptive robotic systems with the ability to combine environment- and task-driven selection pressures: it enables distributed online algorithms for learning behaviours that ensure...
详细信息
ISBN:
(纸本)9781467375351
The MONEE framework endows collective adaptive robotic systems with the ability to combine environment- and task-driven selection pressures: it enables distributed online algorithms for learning behaviours that ensure both survival and accomplishment of user-defined tasks. This paper explores the trade-off between these two requirements that evolution must establish when the task is detrimental to survival. To this end, we investigate experiments with populations of 100 simulated robots in a foraging task scenario where successfully collecting resources negatively impacts an individual's remaining lifetime. We find that the population remains effective at the task of collecting pucks even when the negative impact of collecting a puck is as bad as halving the remaining lifetime. A quantitative analysis of the selection pressures reveals that the task-based selection exerts a higher pressure than the environment.
暂无评论