This paper presents a continuous-time multi-agent network for distributed least absolute deviation (DLAD). The objective function of the DLAD problem is a sum of many least absolute deviation functions. In the multi-agent network, each agent connects with its neighbors locally and they cooperate to obtain the optimal solutions with consensus. The proposed multi-agent network is in fact a collective system with each agent being considered as a recurrent neural network. Simulation results on a numerical example are presented to illustrate the effectiveness and characteristics of the proposed distributed optimization method.
CITATION STYLE
Liu, Q., Zhao, Y., & Cheng, L. (2015). Continuous-time multi-agent network for distributed least absolute deviation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9377 LNCS, pp. 436–443). Springer Verlag. https://doi.org/10.1007/978-3-319-25393-0_48
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