Generalized Pitman-Stanley polytope: vertices and faces
William T. Dugan, Maura Hegarty, Alejandro H. Morales, Annie Raymond

TL;DR
This paper generalizes the Pitman-Stanley polytope to include entries up to m, explores its vertices and faces, and connects it to flow polytopes, plane partitions, and Young tableaux, providing new formulas and characterizations.
Contribution
It introduces a new generalization of the Pitman-Stanley polytope, characterizes its vertices, and relates its structure to flow polytopes and combinatorial objects.
Findings
Number of vertices is a polynomial in m with leading term counting standard Young tableaux.
Provides formulas for the number of faces and generating functions for vertices.
Shows the generalized polytope can be realized as a flow polytope of a grid graph.
Abstract
In 1999, Pitman and Stanley introduced the polytope bearing their name along with a study of its faces, lattice points, and volume. The Pitman-Stanley polytope is well-studied due to its connections to probability, parking functions, the generalized permutahedra, and flow polytopes. Its lattice points correspond to plane partitions of skew shape with entries 0 and 1. Pitman and Stanley remarked that their polytope can be generalized so that lattice points correspond to plane partitions of skew shape with entries . Since then, this generalization has been untouched. We study this generalization and show that it can also be realized as a flow polytope of a grid graph. We give multiple characterizations of its vertices in terms of plane partitions of skew shape and integer flows. For a fixed skew shape, we show that the number of vertices of this polytope is a polynomial…
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
TopicsAdvanced Combinatorial Mathematics · Bayesian Methods and Mixture Models
