How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
An attaining coupling and potential on a three-point metric space
Example
Let have the metric with zero diagonal and , , with symmetric values. For and , the coupling
and normalized potential both attain value .
The straight segments show the two distance-one edges, the node labels give the source and target masses, and the curved arrows represent the two positive coupling entries.
Example references
Facts & Assumptions
Given: The specified three-point distance matrix and the probability vectors in the Example.
The finite-metric theorem equates the minimum coupling cost and maximum normalized 1-Lipschitz objective and gives complementary slackness on positive coupling entries (Finite-metric transport duality and complementary slackness).
A coupling is a nonnegative matrix with prescribed row and column sums, and its cost is the finite sum of distance times transported mass (Couplings of finite probability vectors, The finite-metric 1-Wasserstein transport cost).
A probability vector has nonnegative coordinates summing to one (Finite probability vectors).
Verification
The specified distances are symmetric, vanish exactly on equal points, and are positive for distinct points. For the three distinct points, the triangle inequalities are , , and ; when a point repeats, each inequality is or . Thus this is a metric. The displayed matrix is nonnegative, has row sums and column sums , and has cost .
The vector has . Its pairwise differences have magnitudes , bounded respectively by distances . Its objective is .
The feasible plan and potential in steps 1.1 and 1.2 have the same value. By [F1], the primal optimum is at most the plan cost, the dual optimum is at least the potential value, and the two optima are equal; hence both are attained with value . The positive entries are , and they satisfy and , as complementary slackness requires.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
17 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Gabriel Peyré and Marco Cuturi, Computational Optimal Transport, §§3.3 and 6.1 (standard reference, not scraped)
- Filippo Santambrogio, Introduction to Optimal Transport Theory, §1.3, PDF pp. 5–6 (standard reference, not scraped)