“…A direct computation of α(J(n, w, i)) for all w, i turns successful only up to n = 10 (we have performed the computations using the package GRAPE of the computational system GAP, that deals with graphs with symmetries [28]; indeed, the graphs J(n, w, i) are invariant under the group of permutations of the n coordinates). On the other hand, for the graphs J(n, 3, 1), there is an explicit formula due to Erdős and Sós (see [14,Lemma 18]), but the number of vertices in this case, which is roughly equal to n 3 , it too small to lead to a good bound.…”
Section: Numerical Results For Dimensions Up To 24mentioning
ABSTRACT. We improve by an exponential factor the best known asymptotic upper bound for the density of sets avoiding 1 in Euclidean space. This result is obtained by a combination of an analytic bound that is an analogue of Lovász theta number and of a combinatorial argument involving finite subgraphs of the unit distance graph. In turn, we straightforwardly obtain an asymptotic improvement for the measurable chromatic number of Euclidean space. We also tighten previous results for the dimensions between 4 and 24.
“…A direct computation of α(J(n, w, i)) for all w, i turns successful only up to n = 10 (we have performed the computations using the package GRAPE of the computational system GAP, that deals with graphs with symmetries [28]; indeed, the graphs J(n, w, i) are invariant under the group of permutations of the n coordinates). On the other hand, for the graphs J(n, 3, 1), there is an explicit formula due to Erdős and Sós (see [14,Lemma 18]), but the number of vertices in this case, which is roughly equal to n 3 , it too small to lead to a good bound.…”
Section: Numerical Results For Dimensions Up To 24mentioning
ABSTRACT. We improve by an exponential factor the best known asymptotic upper bound for the density of sets avoiding 1 in Euclidean space. This result is obtained by a combination of an analytic bound that is an analogue of Lovász theta number and of a combinatorial argument involving finite subgraphs of the unit distance graph. In turn, we straightforwardly obtain an asymptotic improvement for the measurable chromatic number of Euclidean space. We also tighten previous results for the dimensions between 4 and 24.
“…To our best knowledge, the SubSemi package is the first software tool capable of calculating general semigroup embeddings. For the isomorphism calculation, we can compare our method to SmallestMultiplicationTable function in the Semigroups package [25] (based on the SmallestImageSet function in Gap [15] and depending on the GRAPE package [27]). This function calculates the smallest multiplication table in lexicographic ordering for the semigroup (by calculating a group action orbit of the symmetric group), which can be used for isomorphism testing.…”
We present a search algorithm for constructing embeddings and deciding isomorphisms of semigroups, working with their multiplication tables. The algorithm is used for enumerating diagram semigroups up to isomorphism and for finding minimal degree representations.
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