Representability of Lyndon-Maddux relation algebras
Jeremy F. Alm

TL;DR
This paper investigates the conditions under which Lyndon-Maddux relation algebras are representable, narrowing the known gap between non-representability and representability thresholds.
Contribution
It improves previous bounds by proving that for q at least n times a logarithmic factor, the algebra is representable.
Findings
If q ≥ n(log n)^{1+ε}, the algebra is representable.
Previous bounds were q > 2304n^2+1 for representability.
Non-representability holds if q < 2n.
Abstract
In Alm-Hirsch-Maddux (2016), relation algebras were defined that generalize Roger Lyndon's relation algebras from projective lines, so that is a Lyndon algebra. In that paper, it was shown that if , is representable, and if , is not representable. In the present paper, we reduced this gap by proving that if , is representable.
| union bound | local lemma | |
|---|---|---|
| 2 | 27 | 23 |
| 3 | 59 | 41 |
| 4 | 89 | 61 |
| 5 | 121 | 83 |
| 6 | 157 | 107 |
| 7 | 191 | 131 |
| 8 | 227 | 157 |
| 9 | 263 | 179 |
| 10 | 307 | 211 |
| 11 | 343 | 233 |
| 12 | 379 | 257 |
| 13 | 419 | 289 |
| 14 | 461 | 311 |
| 15 | 503 | 343 |
| 16 | 547 | 367 |
| 17 | 587 | 397 |
| 18 | 631 | 431 |
| 19 | 673 | 457 |
| 20 | 719 | 487 |
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Taxonomy
TopicsBiological Activity of Diterpenoids and Biflavonoids
Representability of Lyndon-Maddux relation algebras
Jeremy F. Alm
(March 2017)
Abstract
In Alm-Hirsch-Maddux (2016), relation algebras were defined that generalize Roger Lyndon’s relation algebras from projective lines, so that is a Lyndon algebra. In that paper, it was shown that if , is representable, and if , is not representable. In the present paper, we reduced this gap by proving that if , is representable.
1 Introduction
Let RA denote the class of relation algebras, let RRA denote the class of representable relation algebras, and let wRRA denote the class of weakly representable relation algebras. Then we have , and RRA (resp., wRRA) is not finitely axiomatizable over wRRA (resp., RA). Jónsson [4] showed that every equational basis for RRA contains equations with arbitrarily many variables. Since all three of RA, wRRA, and RRA are varieties, we may infer from Jónsson’s result either that every equational basis for wRRA contains equations with arbitrarily many variables or every equational basis defining RRA over wRRA contains equations with arbitrarily many variables. In [1], it is shown that the latter holds. (Whether the former holds is still open.)
The argument in [1] uses a construction of arbitrarily large finite weakly representable but not representable relation algebras whose “small” subalgebras are representable. Define a class of algebras as follows.
Definition 1**.**
Let be a positive integer, and a nonnegative integer. Then is the symmetric integral relation algebra with atoms . Write and , and define on atoms as follows: if , , , and , then
[TABLE]
Definition 1 from [1] is due to Maddux. When , is the Lyndon algebra from a projective line. is representable over points exactly when there exists a projective plane of order . For , the idea is to take a Lyndon algebra , add an additional atom and mandatory cycles , where is any atom besides , to get . For , split (in the sense of [3]) into smaller atoms. Even if is representable, splitting into too many pieces can destroy representability (although in this particular case splitting does preserve weak representability). In fact, we have the following theorem from [1].
Theorem 2**.**
Let be a prime power, so that is representable. Then
- (i.)
if , then ; 2. (ii.)
If , then .
Our concern in the present paper is this: How large does need to be, relative to , to guarantee that if is a prime power, ? The sole result of the present paper is an improvement of the bound in Theorem 2 (ii.), as follows.
Theorem 3**.**
Let , and let be a prime power with . Then for sufficiently large , . In particular, for , if , .
2 The union bound and the Lovász local lemma
In this section we will establish the necessary background in probabilistic combinatorics. We will do this somewhat informally, since there are plenty of rigorous references on the subject, like [2, 5].
Consider a random combinatorial structure , along with a probability space corresponding to all possible particular instances of . (For example, for the standard random graph model , where edges are either included or excluded independently with probability 0.5, the probability space would be the space of all graphs with vertices.) Suppose that we want to show that can have a property . Suppose further that is a list of events , the “bad” events, that prevent from being attained. (For example, if is the property of being bipartite, then the events are the instances of odd cycles.) We would like to estimate the probability that any of the bad events occur. In particular, in many cases one may show as the size of a random combinatorial structure grows, but all we really need for a nonconstructive existence proof is to show . The following two tools are widely used to this end.
Union Bound
For events ,
[TABLE]
Informally, we may think of the union bound as a simpleton’s inclusion-exclusion principle, whereby we throw away all the terms involving intersections.
Lovász local lemma
If the each occur with probability at most , and with each event independent of all other ’s except at most of them, then if , then . (Here, is the base of the natural logarithm.)
3 Proof of main result
Consider , where is the field with elements. For convenience, let . For each , define
[TABLE]
and
[TABLE]
One can think of as the union of lines with slope , and of as the union of vertical lines, in . As is well-known, the map is a representation of . (This was originally shown in [6].) Write . Let be a disjoint copy of , and let be the corresponding copy of . Then the map that sends and to is a representation of . (See [1].)
Now we define a random structure . Take the (image of the) representation of , and view it as a complete graph with vertex set . Consider all possible ways of coloring the -edges in colors . We show that the resulting probability space contains a representation of .
We need to check that all of the following conditions obtain:
[TABLE]
[TABLE]
If there is an edge , labelled , say, for which there is no such that is labelled and is labelled , we will say that that edge fails. (In general, conditions (1) and (2) give each edge some “needs”, and we will say that an edge fails if it has any unmet needs.)
Fix an edge labelled . Each vertex has edges colored in -colors, so the probability that (1) fails on a given -edge for colors is . Hence the failure for any such pair is bounded above (via the union bound) by
[TABLE]
Similarly, fix an edge colored . Each vertex has degree in each color, and degree in the various colors. Let the edge colored be . The probability that there is no with colored and colored for fixed is . Hence the failure for all is bounded by . Since we must also consider such that is colored and is colored , we double to get . Now, we may bound the probability that there exists an edge that fails. Since there are -labelled edges and -labelled edges, the union bound gives
[TABLE]
We want to make (3) , so there exists positive probability that no edge fails. So far, so good, but we can do better using the local lemma. (In particular, see Table 1 and Figure 1.)
In order to make use of the local lemma, we need to count dependencies. For any edge , the failure of that edge is dependent on all the -labelled edges incident to and , as well as all of the -labelled edges. So we bound this by , twice the total number of vertices.
We need . Consequently, it must be the case that
[TABLE]
So we want
[TABLE]
Taking logs of both sides and manipulating, we get
[TABLE]
Since , we need to take slightly larger than , so that the RHS will eventually beat the on the LHS.
Taking , , will do for sufficiently large (depending on ). See Figure 2 for the dependence of upon .
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1[1] Jeremy F. Alm, Robin Hirsch, and Roger D. Maddux. There is no finite-variable equational axiomatization of representable relation algebras over weakly representable relation algebras. Rev. Symb. Log. , 9(3):511–521, 2016.
- 2[2] Noga Alon and Joel H. Spencer. The probabilistic method . Wiley Series in Discrete Mathematics and Optimization. John Wiley & Sons, Inc., Hoboken, NJ, fourth edition, 2016.
- 3[3] H. Andréka, R. D. Maddux, and I. Németi. Splitting in relation algebras. Proc. Amer. Math. Soc. , 111(4):1085–1093, 1991.
- 4[4] Bjarni Jónsson. The theory of binary relations. In Algebraic logic (Budapest, 1988) , volume 54 of Colloq. Math. Soc. János Bolyai , pages 245–292. North-Holland, Amsterdam, 1991.
- 5[5] Stasys Jukna. Extremal combinatorics . Texts in Theoretical Computer Science. An EATCS Series. Springer, Heidelberg, second edition, 2011. With applications in computer science.
- 6[6] R. C. Lyndon. Relation algebras and projective geometries. Michigan Math. J. , 8:21–28, 1961.
