2014
DOI: 10.4171/jems/484
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A density version of the Carlson–Simpson theorem

Abstract: We prove a density version of the Carlson-Simpson Theorem. Specifically we show the following. For every integer k 2 and every set A of words over k satisfying lim sup n→∞ |A ∩ [k] n | k n > 0 there exist a word c over k and a sequence (wn) of left variable words over k such that the set {c} ∪ c w 0 (a 0 ) ... wn(an) : n ∈ N and a 0 , ..., an ∈ [k]is contained in A.While the result is infinite-dimensional its proof is based on an appropriate finite and quantitative version, also obtained in the paper.2000 Math… Show more

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Cited by 10 publications
(20 citation statements)
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“…Theorem 2.8 is the density version of a well-known coloring result due to T. J. Carlson and S. J. Simpson [4]. Also we notice that the argument in [7] is effective and gives explicit upper bounds for the numbers DCS(k, m, δ). These upper bounds, however, have an Ackermann-type dependence with respect to k.…”
Section: Probabilistic Preliminariesmentioning
confidence: 74%
See 1 more Smart Citation
“…Theorem 2.8 is the density version of a well-known coloring result due to T. J. Carlson and S. J. Simpson [4]. Also we notice that the argument in [7] is effective and gives explicit upper bounds for the numbers DCS(k, m, δ). These upper bounds, however, have an Ackermann-type dependence with respect to k.…”
Section: Probabilistic Preliminariesmentioning
confidence: 74%
“…On the proof of Theorem 1.2. The first basic ingredient of the proof of Theorem 1.2 is the density version of the Carlson-Simpson Theorem established, recently, in [7]. The second basic ingredient is a partition result closely related to the work of T. J. Carlson [3], and H. Furstenberg and Y. Katznelson [9].…”
mentioning
confidence: 94%
“…There is a natural extension of Theorem 1 which deals simultaneously with a family of random variables. Although in applications one usually encounters only finite families of random variables (see, e.g., [5]), the cleanest formulation of this extension is for stochastic processes indexed by the sample space of a probability space (T, Σ, µ). Specifically, we have the following theorem.…”
Section: 2mentioning
confidence: 99%
“…Theorem 1 was motivated by these results and was found in an effort to abstract their probabilistic features. We expect that Theorem 1 will in turn facilitate further applications, possibly even beyond the combinatorial context of [4,5].…”
Section: Introductionmentioning
confidence: 98%
“…This methodology has been applied successfully to related problems in Ramsey Theory -see, in particular, [6].…”
Section: Consequencesmentioning
confidence: 99%