Daniel Carter, Kevin O’Bryant, and I have uploaded to the arXiv our paper On the diameter and distribution of Sidon sets. This is a paper stemming from an interaction on my blog last year, regarding t...

Daniel Carter, Kevin O’Bryant, and I have uploaded to the arXiv our paper On the diameter and distribution of Sidon sets. This is a paper stemming from an interaction on my blog last year, regarding the use of AlphaEvolve to improve the then state of the art on the size of Sidon sets. We obtained a series of further refinements to the bound until progress stalled; after several months of no further progress, we decided to release what we have in this preprint.
Recall that a Sidon set
is a set of integers with the property that all the sums
with
and
are distinct. Equivalently, all the differences
,
are distinct, except at
where of course there is a large multiplicity. A basic question (Erdős problem #30) asks for the largest size
of a Sidon set in
. Several algebraic constructions of Sidon sets (by Singer, Bose, and Ruzsa) are known with
and special choices of
; see this survey of O’Bryant for details. Erdős and Turán have conjectured that
cannot exceed
, with the bound
also proposed by Erdős as “perhaps too optimistic”.
For some time, the best upper bound was of the shape 

To illustrate our approach, let us sketch a proof of the bound (1). The fact that all the non-zero differences of
are disjoint gives rise to a pointwise convolution bound

denotes the reflection of
. Taking inner products with a non-negative autocorrelation function
to be chosen later, we conclude that 
with a further weight
, and rearranging, we conclude the bound 

, then the left-hand side is at least
. This gives an inequality 
by choosing
appropriately. For instance if one selects ![\displaystyle w := 1_{[1, k^{3/2}]}](/articles/258/eCzDdyuTcM9CO9bMIHhAF1TNA0hKmAGh.webp)
![\displaystyle t := 1_{[-k^{3/2}, N]}](/articles/258/wvTON0MillLDSBM3BrnDvd4MuXu7vkIP.webp)
, and the above bound becomes (after ignoring some lower order terms) 
One can improve this bound by replacing the sharply truncated cutoff functions
with more complicated cutoffs, which obey some biconvex constraints which one can explore using various optimization algorithms. It turns out that one can do even better by working not with deterministic choices of
, but with random choices of
, in which inequalities such as
are only true after performing an expectation. The calculations get quite messy (and highly computer-assisted), but eventually give (2). The method also extends to the more general class of
-Golomb rulers, in which a given non-zero difference is permitted to appear up to
times.
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