The random generation of Latin rectangles based on the assignment problem

We investigate the random generation of Latin rectangles using a method based on the assignment problem. Specifically, each row is selected via a minimum-cost permutation from a randomly generated cost matrix. This approach defines a convex polytope for each Latin rectangle, where the volume of the polytope determines the sampling probability of the corresponding rectangle. Our analysis reveals that the resulting process is efficient but generally non-uniform, thereby providing a negative answer to a question posed by the second author in 2009. Furthermore, we establish that the volume of the polytope is invariant under column and symbol permutations of the rectangles.

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Publication Details

Journal
Discrete Applied Mathematics
Published
2025-08-08
DOI
https://doi.org/10.1016/j.dam.2025.07.049
Citations
1
Primary Topic
Computational Geometry and Mesh Generation
Type
article
Field-Weighted Citation Impact
3.54
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article

The random generation of Latin rectangles based on the assignment problem

Farheen Iftikhar, Gábor P. Nagy
1 citations
Discrete Applied Mathematics
Computational Geometry and Mesh Generation
3.54
article

The random generation of Latin rectangles based on the assignment problem

Farheen Iftikhar, Gábor P. Nagy
article en
1 citations

Abstract

We investigate the random generation of Latin rectangles using a method based on the assignment problem. Specifically, each row is selected via a minimum-cost permutation from a randomly generated cost matrix. This approach defines a convex polytope for each Latin rectangle, where the volume of the polytope determines the sampling probability of the corresponding rectangle. Our analysis reveals that the resulting process is efficient but generally non-uniform, thereby providing a negative answer to a question posed by the second author in 2009. Furthermore, we establish that the volume of the polytope is invariant under column and symbol permutations of the rectangles.

Discrete Applied MathematicsVol. 378
University of Szeged (HU), Budapest University of Technology and Economics (HU)
Openalex Percentile: Top 8%
Computational Geometry and Mesh Generation
3.54
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