Pith. sign in

REVIEW 2 cited by

Combinatorics and Geometry of Transportation Polytopes: An Update

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1307.0124 v1 pith:6LTZWZW4 submitted 2013-06-29 math.CO

Combinatorics and Geometry of Transportation Polytopes: An Update

classification math.CO
keywords polytopescombinatoricsgeometrynaturallysquaressurveytransportationupdate
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

A transportation polytope consists of all multidimensional arrays or tables of non-negative real numbers that satisfy certain sum conditions on subsets of the entries. They arise naturally in optimization and statistics, and also have interest for discrete mathematics because permutation matrices, latin squares, and magic squares appear naturally as lattice points of these polytopes. In this paper we survey advances on the understanding of the combinatorics and geometry of these polyhedra and include some recent unpublished results on the diameter of graphs of these polytopes. In particular, this is a thirty-year update on the status of a list of open questions last visited in the 1984 book by Yemelichev, Kovalev and Kravtsov and the 1986 survey paper of Vlach.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Fixed points in de Finetti hierarchies

    quant-ph 2026-07 accept novelty 7.0

    Fixed-point constraints on de Finetti hierarchies yield O(√(log n)/n) double-sided rates, block-structured dimension dependence, and poly-time certifiable separable inner approximations for fixed local dimensions.

  2. Explaining Machine Learning and Memorization with Statistical Mechanics

    cs.LG 2026-06 unverdicted novelty 3.0

    Thesis uses statistical mechanics to study DAM and RBM models for understanding memorization, low-dimensional learning, and adversarial robustness in neural networks.