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Abstract

This paper studies a combined space partitioning and network flow optimization problem, with applications to large-scale power, transportation, or communication systems. In dense wireless networks, one may want to simultaneously optimize the assignment of many spatially distributed users to base stations and route the resulting communication traffic through the backbone network. We formulate the overall problem by coupling a semi-discrete optimal transport (SDOT) problem, capturing the space partitioning component, with a minimum-cost flow problem on a discrete network. This formulation jointly optimizes the assignment of a continuous demand distribution to certain endpoint network nodes and the routing of flows over the network to serve the demand, under capacity constraints. As for SDOT problems, we show that the formulation of our problem admits a tight relaxation taking the form of an infinite-dimensional linear program, derive a finite-dimensional dual problem, and show that strong duality holds. We leverage these results to design a distributed dual gradient ascent algorithm to solve the problem, where nodes in the graph perform computations based solely on locally available information. Simulation results illustrate the algorithm performance and its applicability to an electric power distribution network reconfiguration problem. This version extends the CDC 2025 conference paper with additional proof sketches.