On the existence of Monge solutions to multi-marginal optimal transport with quadratic cost and uniform discrete marginals
Bibliographic record
Abstract
A natural and important question in multi-marginal optimal transport is whether the \emph{Monge ansatz} is justified; does there exist a solution of Monge, or deterministic, form? We address this question for the quadratic cost when each marginal measure is $m$-empirical (that is, uniformly supported on $m$ points). By direct computation, we provide an example showing that the ansatz \emph{can fail} when the underlying dimension $d$ is $2$, number of marginals $N$ to be matched is $3$ and the size $m$ of their supports is $3$. As a consequence, the set of $m$-empirical measures is not barycentrically convex when $N \geq 3$, $d \geq 2$ and $m \geq3$. It is a well known consequence of the Birkhoff-von Neumann Theorem that the Monge ansatz holds for $N=2$, standard techniques show it holds when $d=1$, and we provide a simple proof here that \emph{it holds whenever $m=2$}. Therefore, the $N$, $d$ and $m$ in our counterexample are as small as possible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".