When Is the Matching Polytope Box-Totally Dual Integral?
Bibliographic record
Abstract
Let G = (V, E) be a graph. The matching polytope of G, denoted by P(G), is the convex hull of the incidence vectors of all matchings in G. As proved by Edmonds [10] [Edmonds J (1965) Maximum matching and a polyhedron with 0, 1-vertices, J. Res. Nat. Bur. Standards Sect. B 69(1–2):125–130.], P(G) is determined by the following linear system π(G): x(e) ≥ 0 for each e ∈ E; x(δ(v)) ≤ 1 for each v ∈ V; and x(E[U]) ≤ ½|U|⌋ for each U ⊆ V with |U| odd. In 1978, Cunningham and Marsh [6] [Cunningham W, Marsh A (1978) A primal algorithm for optimum matching. Balinski ML, Hoffman AJ, eds. Polyhedral combinatorics. Mathematical Programming Studies, Vol. 8 (Springer, Berlin), 50–72.] strengthened this theorem by showing that π(G) is always totally dual integral. In 1984, Edmonds and Giles [11] [Edmonds J, Giles R (1984) Total dual integrality of linear inequality systems. Progress in Combinatorial Optimization (Academic Press, Toronto), 117–129.] initiated the study of graphs G for which π(G) is box-totally dual integral. In this paper, we present a structural characterization of all such graphs, and develop a general and powerful method for establishing box-total dual integrality. The online appendix is available at https://doi.org/10.1287/moor.2017.0852 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".