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Distributed MIN-MAX Optimization Application to Time-optimal Consensus: An Alternating Projection Approach

机译:MIN-MAX分布式优化在时间最优共识中的应用:一种交替投影方法

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In this paper, we proposed an alternating projection based algorithm to solve a class of distributed MIN-MAX convex optimization problems. We firstly transform this MIN-MAX problem into the problem of searching for the minimum distance between some hyper-plane and the intersection of the epigraphs of convex functions. The Bregmans alternating method is employed in our algorithm to achieve the distance by iteratively projecting onto the hyper-plane and the intersection. The projection onto the intersection is obtained by cyclic Dykstras projection method. We further apply our algorithm to the minimum time multi-agent consensus problem. The attainable states set for the agent can be transformed into the epigraph of some convex functions, and the search for time-optimal state for consensus satisfies the MIN-MAX problem formulation. Finally, the numerous simulation proves the validity of our algorithm.
机译:在本文中,我们提出了一种基于交替投影的算法来解决一类分布式MIN-MAX凸优化问题。我们首先将这个MIN-MAX问题转化为寻找某些超平面与凸函数的凸形标点的交点之间的最小距离的问题。我们的算法采用Bregmans交替方法,通过迭代投影到超平面和交点上来实现距离。通过循环Dykstras投影方法获得到交叉点的投影。我们进一步将算法应用于最短时间的多主体共识问题。可以将为主体设置的可达到状态转换成某些凸函数的题词,并且寻找时间最优状态以达成共识,从而满足MIN-MAX问题的提法。最后,大量的仿真证明了该算法的有效性。

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