Optimally Solving a Transportation Problem Using Voronoi Diagrams

In this paper we consider the following variant of the well-known Monge-Kantorovich transportation problem. Let S be a set of n point sites in ℝ d . A bounded set C ⊂ ℝ d is to be distributed among the sites p ∈ S such that (i), each p receives a subset C p of prescribed volume and (ii), the average distance of all points z of C from their respective sites p is minimized. In our model, volume is quantified by a measure μ, and the distance between a site p and a point z is given by a function d p (z). Under quite liberal technical assumptions on C and on the functions d p (·) we show that a solution of minimum total cost can be obtained by intersecting with C the Voronoi diagram of the sites in S, based on the functions d p (·) equipped with suitable additive weights. Moreover, this optimum partition is unique, up to subsets of C of measure zero. Unlike the deep analytic methods of classical transportation theory, our proof is based on direct geometric arguments.

[1]  Franz Aurenhammer,et al.  Minkowski-Type Theorems and Least-Squares Clustering , 1998, Algorithmica.

[2]  Günter Rote Two Applications of Point Matching , 2009, Computational Geometry.

[3]  Pravin M. Vaidya,et al.  Geometry helps in matching , 1989, STOC '88.

[4]  Günter Rote,et al.  Optimally solving a transportation problem using Voronoi diagrams , 2012, Comput. Geom..

[5]  R. Ho Algebraic Topology , 2022 .

[6]  W. Gangbo,et al.  The geometry of optimal transportation , 1996 .

[7]  Franz Aurenhammer,et al.  Voronoi Diagrams , 2000, Handbook of Computational Geometry.

[8]  L. Kantorovich On a Problem of Monge , 2006 .

[9]  Pankaj K. Agarwal,et al.  Algorithms for the transportation problem in geometric settings , 2012, SODA.

[10]  Leonidas J. Guibas,et al.  A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[11]  C. Villani Optimal Transport: Old and New , 2008 .