Volume 27, 2021
|Number of page(s)||32|
|Published online||22 July 2021|
A mass reducing flow for real-valued flat chains with applications to transport networks*
Hendrix College, 1600 Washington Avenue,
** Corresponding author: email@example.com
Accepted: 28 June 2021
An oriented transportation network can be modeled by a 1-dimensional chain whose boundary is the difference between the demand and supply distributions, represented by weighted sums of point masses. To accommodate efficiencies of scale into the model, one uses a suitable Mα norm for transportation cost for α ∈ (0, 1]. One then finds that the minimal cost network has a branching structure since the norm favors higher multiplicity edges, representing shared transport. In this paper, we construct a continuous flow that evolves some initial such network to reduce transport cost without altering its supply and demand distributions. Instead of limiting our scope to transport networks, we construct this Mα mass reducing flow for real-valued flat chains by finding a higher dimensional real chain whose slices dictate the flow. Keeping the boundary fixed, this flow reduces the Mα mass of the initial chain and is Lipschitz continuous under the flat-α norm. To complete the paper, we apply this flow to transportation networks, showing that the flow indeed evolves branching transport networks to be more cost efficient.
Mathematics Subject Classification: 49Q20 / 49Q10
Key words: Optimal transport networks / mass reducing flows / flat chains
I wish to express my gratitude to Rice University for their support during much of this research, and specifically, Dr. Robert Hardt for his guidance. I’d also like to thank Dr. Christopher Camfield for his thoughts during the writing of this paper. Finally, I thank the referees for their insights and corrections.
© The authors. Published by EDP Sciences, SMAI 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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