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This paper introduces Control Laguerre Tessellation (CLT), a mathematical framework that applies optimal transport theory to optimally controlled agents using motion-induced ground costs. By generalizing Laguerre tessellations for linear agents with minimum energy or time objectives, the research bridges systems control and machine learning through discrete target measure analysis. The work is significant for advancing multiagent system optimization where agent dynamics directly define the transport geometry.
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Control Laguerre Tessellation: Semi-discrete Optimal Transport Over Control Systems

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2607.09139v2 Announce Type: replace-cross Abstract: We study the optimal transport of optimally controlled agents from a compactly supported absolutely continuous source to a discrete target measure. The ground cost for the transport is induced by the optimal cost of the agents' motion. When this ground cost satisfies the twist condition, the optimal transport map is given almost everywhere in terms of a Laguerre tessellation of the state space. We refer to this control-theoretic generalization of Laguerre tessellation as Control Laguerre Tessellation (CLT), and illustrate it for two ground costs induced by linear controlled agents with minimum energy and minimum time objectives.

Abstract:We study the optimal transport of optimally controlled agents from a compactly supported absolutely continuous source to a discrete target measure. The ground cost for the transport is induced by the optimal cost of the agents' motion. When this ground cost satisfies the twist condition, the optimal transport map is given almost everywhere in terms of a Laguerre tessellation of the state space. We refer to this control-theoretic generalization of Laguerre tessellation as Control Laguerre Tessellation (CLT), and illustrate it for two ground costs induced by linear controlled agents with minimum energy and minimum time objectives.

Optimization and Control (math.OC); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Systems and Control (eess.SY)

arXiv:2607.09139 [math.OC]

(or arXiv:2607.09139v2 [math.OC] for this version)

https://doi.org/10.48550/arXiv.2607.09139 Focus to learn more arXiv-issued DOI via DataCite

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