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- W2948477430 abstract "In this work, we propose and investigate a scalable, distributed iterative algorithm for large-scale optimal transport of collectives of autonomous agents. We formulate the problem as one of steering the collective towards a target probability measure while minimizing the total cost of transport, with the additional constraint of distributed implementation imposed by a range-limited network topology. Working within the framework of optimal transport theory, we realize the solution as an iterative transport based on a proximal point algorithm. At each stage of the transport, the agents implement an online, distributed primal-dual algorithm to obtain local estimates of the Kantorovich potential for optimal transport from the current distribution of the collective to the target distribution. Using these estimates as their local objective functions, the agents then implement the transport by a proximal point algorithm. This two-step process is carried out recursively by the collective to converge asymptotically to the target distribution. We analyze the behavior of the algorithm via a candidate system of feedback interconnected PDEs for the continuous time and $N rightarrow infty$ limit, and establish the asymptotic stability of this system of PDEs. We then test the behavior of the algorithm in simulation." @default.
- W2948477430 created "2019-06-14" @default.
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- W2948477430 date "2018-04-04" @default.
- W2948477430 modified "2023-10-18" @default.
- W2948477430 title "Distributed Online Optimization for Multi-Agent Optimal Transport" @default.
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