A class of multi-marginal c-cyclically monotone sets with explicit c-splitting potentials
Sedi Bartz, Heinz H. Bauschke, Xianfu Wang

TL;DR
This paper introduces a new, easier-to-verify condition for constructing c-splitting potentials in multi-marginal optimal transport, extending classical convex analysis and applicable to certain cost functions and set margins.
Contribution
It provides an explicit construction method for c-splitting potentials under a new sufficient condition for multi-marginal c-cyclically monotone sets.
Findings
The new condition is sufficient for explicit c-splitting potential construction.
The condition is easier to verify than multi-marginal c-cyclic monotonicity.
When margins are one-dimensional, the condition characterizes c-splitting sets.
Abstract
Multi-marginal optimal transport plans are concentrated on c-splitting sets. It is known that, similar to the two-marginal case, c-splitting sets are c-cyclically monotone. Within a suitable framework, the converse implication was very recently established by Griessler. However, for an arbitrary cost c, given a multi-marginal c-cyclically monotone set, the question whether there exists an analogous explicit construction to the one from the two-marginal case of c-splitting potentials is still open. When the margins are one-dimensional and the cost belongs to a certain class, Carlier proved that the two-marginal projections of a c-splitting set are monotone. For arbitrary products of sets equipped with cost functions which are sums of two-marginal costs, we show that the two-marginal monotonicity condition is a sufficient condition which does give rise to an explicit construction of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPoint processes and geometric inequalities · Markov Chains and Monte Carlo Methods · Geometric Analysis and Curvature Flows
