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A near-linear time approximation scheme for geometric transportation with arbitrary supplies and spread

The geometric transportation problem takes as input a set of points $P$ in $d$-dimensional Euclidean space and a supply function $μ: P \to \mathbb{R}$. The goal is to find a transportation map, a non-negative assignment $τ: P \times P \to \mathbb{R}_{\geq 0}$ to pairs of points, so the total assignment leaving each point is equal to its supply, i.e., $\sum_{r \in P} τ(q, r) - \sum_{p \in P} τ(p, q) = μ(q)$ for all points $q \in P$. The goal is to minimize the weighted sum of Euclidean distances for the pairs, $\sum_{(p, q) \in P \times P} τ(p, q) \cdot ||q - p||_2$. We describe the first algorithm for this problem that returns, with high probability, a $(1 + \varepsilon)$-approximation to the optimal transportation map in $n\varepsilon^{-O(d)}\log^{O(d)}{n}$ time. In contrast to the previous best algorithms for this problem, our near-linear running time bound is independent of the spread of $P$ and the magnitude of its real-valued supplies.