2014
DOI: 10.1002/jcd.21413
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Multidimensional Permanents and an Upper Bound on the Number of Transversals in Latin Squares

Abstract: The permanent of a multidimensional matrix is the sum of products of entries over all diagonals. A nonnegative matrix whose every 1‐dimensional plane sums to 1 is called polystochastic. A latin square of order n is an n×n array of n symbols in which each symbol occurs exactly once in each row and each column. A transversal of such a square is a set of n entries such that no two entries share the same row, column, or symbol. Let T(n) be the maximum number of transversals over all latin squares of order n. Here,… Show more

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Cited by 28 publications
(22 citation statements)
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“…Improving on this, Taranenko [17] recently proved a better upper bound. Before stating her result, we shall need some notation.…”
Section: Introductionmentioning
confidence: 96%
“…Improving on this, Taranenko [17] recently proved a better upper bound. Before stating her result, we shall need some notation.…”
Section: Introductionmentioning
confidence: 96%
“…The problem of an upper bound on the number of transversals in latin squares of order q was posed by Wanless in Loops'03 and soon afterwards the first non-trivial asymptotic bound was proved in [5]. The bound was improved up to (1 + o(1)) q e 2 q in [10], and in [3] it was reproved by another technique. Moreover, in the latter paper it was proposed a probabilistic construction of latin squares that confirmed the exactness of this bound.…”
Section: Introductionmentioning
confidence: 99%
“…, a i n )} q i=1 which do not share a coordinate. As for latin hypercubes, papers [3] and [10] bound the number their transversals by (1 + o(1)) q n−1 e n q for large q.…”
Section: Introductionmentioning
confidence: 99%
“…Cooper and Kovalenko [CK96] showed that c 2 = e −0.08854 is acceptable, and this was later improved by Kovalenko [Kov96] to c 2 = 1/ √ 2 and by McKay, McLeod and Wanless [MMW06] to c 2 = 0.614. More recently, Taranenko [Tar15] proved that one can take c 2 = 1/e + o(1). Glebov and Luria [GL15] proved the same bound using a somewhat simpler method based on entropy.…”
mentioning
confidence: 99%