A generalisation of Varnavides's theorem

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A linear equation <![CDATA[ $E$ ]]> is said to be sparse if there is <![CDATA[ $c\gt 0$ ]]> so that every subset of <![CDATA[ $[n]$ ]]> of size <![CDATA[ $n^{1-c}$ ]]> contains a solution of <![CDATA[ $E$ ]]> in distinct integers. The problem of characterising the sparse equations, first raised by Ruzsa in the 90s, is one of the most important open problems in additive combinatorics. We say that <![CDATA[ $E$ ]]> in <![CDATA[ $k$ ]]> variables is abundant if every subset of <![CDATA[ $[n]$ ]]> of size <![CDATA[ $\varepsilon n$ ]]> contains at least <![CDATA[ $\text{poly}(\varepsilon)\cdot n^{k-1}$ ]]> solutions of <![CDATA[ $E$ ]]>. It is clear that every abundant <![CDATA[ $E$ ]]> is sparse, and Girão, Hurley, Illingworth, and Michel asked if the converse implication also holds. In this note, we show that this is the case for every <![CDATA[ $E$ ]]> in four variables. We further discuss a generalisation of this problem which applies to all linear equations.

Original languageEnglish
JournalCombinatorics Probability and Computing
StateAccepted/In press - 2024


  • Roth
  • Ruzsa
  • supersaturation
  • Varnavides

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Statistics and Probability
  • Computational Theory and Mathematics
  • Applied Mathematics


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