Tensor optimal transport, distance between sets of measures and tensor scaling
This work extends classical optimal transport theory to multiple measures, which could benefit fields like machine learning and statistics, but it is incremental as it generalizes known results from the two-measure case.
The paper tackles the optimal transport problem for more than two discrete measures by formulating it as a linear programming problem on d-tensors, introducing an entropic regularization that leads to tensor scaling, and proposing a variation of the Sinkhorn algorithm with geometric convergence under certain conditions.
We study the optimal transport problem for $d>2$ discrete measures. This is a linear programming problem on $d$-tensors. It gives a way to compute a "distance" between two sets of discrete measures. We introduce an entropic regularization term, which gives rise to a scaling of tensors. We give a variation of the celebrated Sinkhorn scaling algorithm. We show that this algorithm can be viewed as a partial minimization algorithm of a strictly convex function. Under appropriate conditions the rate of convergence is geometric and we estimate the rate. Our results are generalizations of known results for the classical case of two discrete measures.