My experience with Computer Proofs

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To acclimate myself with Computer Proofs, I aimed to write down some basic theorems from my area of research in Lean. I chose Lean primarily because of the very active and helpful community. In fact, everything I did in lean was done alongside Yael Dillies whom I met through Zulip. In what follows below, I will discuss how I went about this and what I took away from the experience.

First of all, what is Lean? It is an interactive theorem prover (such as Isabelle, Coq, and others) . For our purposes, it is a way to input mathematical statements and their corresponding proofs. The power of lean is that it verifies that the proof you enter is indeed correct. The user can write a mathematical statement she wishes to prove. The interactive window then notifies us what goals remain to prove and we can begin working to establish them. Correct proofs are rewarded by a “Proof Complete” (and a festive ūüéČ). Incomplete proofs give unaccomplished goals or errors. To get a feel of how this works, one can load up the natural number game.

An interactive theorem prover offers interesting possibilities. The most obvious is that we can be sure that a proof is correct. Of course this is not completely true, as one has to make sure that previous statements and definitions are indeed correctly stated! One can consult this Quanta article about the impressive computer formalization of some of Peter Scholze’s work.

An even more interesting goal is the hope to automate parts of the mathematical research. One idea, independent of theorem provers, is an advanced autocomplete to be used when writing mathematical papers in latex. The technology to build this already exists and has been applied successfully to programming and is still evolving. For instance, Google’s Minerva has already had some success in using language models to perform mathematical reasoning.

Another goal would be a to automate the process of checking correctness of proofs. An effective type of system would save mathematicians countless hours just in refereeing. This can be viewed loosely as a neural machine translation problem. There has been lots of success in this domain with natural language (say something like English to German) both in research and in practice.

Finally a third, and even more ambitious, goal is for an AI to be able to come up with its own proofs. Needless to say, we are currently at the infancy of this program. The interested reader can consult open AI’s somewhat recent paper on the subject.

For further reading on these sorts of things, I recommend a promising path towards autoformalization and general artificial intelligence by Christian Szegedy. Here Szegedy outlines his thoughts on so-called autoformalization as well as how such a program would fit into the general development of AI.

My first step in getting familiar with lean was to work through the natural number game. This is basically a guided tour for proving some basic statements in lean, avoiding some technical difficulties in the process (for instance, it is browser based). Here one has a goal to prove, and must use the given hypotheses to establish the goal. After each new line of code is written, one can analyze how this changes the hypotheses/goals.

After this, I got lean on my local machine and worked through a tutorial, which is again a guided tour, but a bit more in depth than the natural number game (here I was joined for some of this by Tsutomu Okano). I worked through various problems one might see in undergraduate analysis course relating to sequences, limits, and other topics.

The next leap is to begin to contribute to mathlib. Mathlib is a library of mathematics in lean that hundreds (in my estimation) of people have worked on. This is not an easy step, and to proceed here it helps that lean has a very active community through Zulip. The active community was my main reason for choosing Lean over other interactive theorem provers. For instance, knowledge of the incredibly useful Github is helpful for figuring out what is going on.

After asking around, I was lucky that Bhavik Mehta and Yael Dillies were both knowledgeable with Lean and interested in my main area of research. Moreover, there was already some work in mathlib on additive combinatorics and finite sets, which was very useful to build off of (see this work of Bloom and Mehta). I started off by trying to prove Ruzsa’s triangle inequality.

Theorem 1: Let A , B , C \subset \mathbb{Z} be finite sets. Then

|A-C| |B| \leq |A-B||B-C|. \ \ \ \ \ \spadesuit

I thought this was a good starting point as it is fundamental to the area, whilst having a short proof (i.e. Lemma 2.6 in Tao and Vu). This turned out to be a bit harder than I expected. The argument can be formulated as a double counting argument or showing that a certain function is an injection. It turns out that the former formulation is a bit easier to input into lean due to what is already in mathlib, borrowing some of Bhavik’s work on the sum-product problem (however, I was shown that with enough perseverance one can use the latter proof). Moreover, some very simple things like point-wise set subtraction, had to be added to mathlib. This turns out to be quite a non-trivial task and was completed by Yael. This is perhaps a result of additive combinatorics being a relatively new addition to mathlib.

Next, Yael and I input Plunnecke’s inequality into mathlib. Plunnecke’s inquality is another fundamental result that has a relatively short proof (thanks to Petridis). Here, Yael took over the lean part. Their workflow was way more efficient than mine, as they is a veteran of lean. They have developed a plethora of tricks for finding results in mathlib and even figuring out what to use in mathlib. While they cannot write in lean as quickly as I can Latex, they is not ten times slower.

We eventually made a pull request to mathlib containing Ruzsa’s triangle inquality and Plunnecke’s inequality. Mathematically, I did not gain too much working on this. I believe this is in part because I already had a thorough understanding of the proofs. Nevertheless, thinking of how to actually convert theorems to lean did force me to revisit these classical theorems from multiple perspectives and I could see this being productive as one scales.

Overall, I enjoyed the experience of working in Lean, largely due to the vibrant community. Transferring mathematics to theorem provers requires a unique combination of mathematics and software engineering (both to contribute as well as under the hood). We already have some talented people (both young and old) working on this and I certainly look forward to seeing what will be accomplished along these lines.

On the largest sum-free subset problem in the integers

I recently uploaded “On the largest sum-free subset problem in the integers,” to the arXiv.

Let A \subset \mathbb{Z} be a finite subset of the integers. We say A is sum-free if there are no solutions to

a + b = c,

with a,b,c \in A. We define S(A) to be the size of the largest sum-free subset of A. We seek lower bounds for S(A). It is conjectured that

S(A) \geq (n+C)/3. \ \ \ \ \ (1)

for any C > 0. ErdŇĎs established C=1 is admissible and Bourgain later improved this to C=2. By a construction, Eberhard, Green, and Manners showed that C = o(|A|).

I was originally drawn to this problem for two reasons. The first is that the aforementioned result of ErdŇĎs is the first additive combinatorics result in Tao and Vu’s additive combinatorics book. The second is that Bourgain’s original proof seemed to have a stigma that it was quite difficult.

We now sketch that C=1 is admissible, as shown by ErdŇĎs. The first idea is that the set [1/3,2/3) \subset \mathbb{R}/\mathbb{Z} is sum-free. Thus any subset of this set is also sum-free. Note this set has measure 1/3, which is the same as the multiplicative constant in (1).

The second idea is to randomly map A into [1/3,2/3). Indeed choosing

\theta \in \mathbb{R} / \mathbb{Z}

at random, we consider

\theta \cdot A \cap [1/3,2/3) \subset \mathbb{R}/\mathbb{Z}.

One can check that this set on average has size |A|/3 and as mentioned before, is sum-free.

Bourgain’s work and also our work involves more careful choices of \theta. Underpinning the work is to think of f = [1/3,2/3) - 1/3 as a function, f: \mathbb{R}/\mathbb{Z} \to \mathbb{C}, on the torus and to apply a combination of Fourier techniques and combinatorial techniques.

For a set S, we let

f_S(x) = \sum_{s \in S} f(sx).

Then the ErdŇĎs argument above may be restated as \int f_A = 0. Furthermore, (1) would follow from establishing there is an x\in \mathbb{R}/\mathbb{Z} satisfying

f_A(x) \geq C/3.

One new idea in our work is to partition A into A_0 and A_1, where A_1 is the set of elements in A that are divisible by 3. It turns out that this decomposition is useful as

f_{A_1}(x) = f_{A_1}(x+1/3) = f_{A_1}(x+2/3),


f_{A_0}(x) + f_{A_0}(x+1/3) + f_{A_0}(x+2/3) = 0.

Thus, for instance, a short argument reveals that if one can establish f_{A_1}(x) \geq C/3, then it follows that (1) for A.

Quantitative Hilbert irreducibility and almost prime values of polynomial discriminants

Recently AndersonGafniLemke OliverLowry-DudaZhang and myself uploaded ”Quantitative Hilbert irreducibility and almost prime values of polynomial discriminants” to the arXiv. Let {H} be a large integer,

\displaystyle V_n(H) = \{x^n + a_{n-1} x^{n-1} + \cdots + a_0 : a_{n-1} , \ldots , a_0 \in \mathbb{Z} \cap [-H,H]\},


\displaystyle E_n(H) = \#\{f \in V_n(H) : {\rm Gal}(f) \neq S_n\}.

A classical principle going back to at least Hilbert is that a typical polynomial should have Galois group {S_n}, and this would suggest {\#E_n(H)} is significantly smaller than {\#V_n(H)}. Van der Waerden conjectured that {E_n(H) = o(\#V_n(H) /H)}. Van der Waerden’s conjecture was almost proved quite recently by Chow and Dietmann (for {n \neq 7,8,10}), except they are not able to estimate 

\displaystyle E_n(H , A_n) = \#\{f \in V_n(H) : {\rm Gal}(f) = A_n\}.

Gallagher showed, using the large sieve, that 

\displaystyle E_n(H) = O\left(\frac{\#V_n(H) \log H}{H^{1/2}} \right).\ \ \ \ \ (1)

We show the following.

{\ } Theorem 1: Let {H} be a positive integer. then 

\displaystyle \#E_n(H , A_n) = O\left(\frac{\#V_n(H) H^{O(1/n)}}{H^{2/3}} \right). \ \ \ \spadesuit{\ }

We note that Bhargava recently announced a proof of {E_n(H , A_n) = O(\#V_n(H) / H)}, which is stronger than Theorem 1. We remark that our techniques also apply to counting polynomials with almost prime discriminants (see Theorem 1.2 and the related 1.3). 

We briefly mention some of the ideas used to prove Theorem 1. Classical algebraic number theory reveals that if the Galois group of a polynomial {f} is {A_n}, then {\mu_p(f) = (-1)^{n+1}}, where {\mu_p} is the Mobius function for {\mathbb{F}_p[x]}. Thus we can employ a sieve to estimate {\#E_n(H , A_n) }. However, instead of using the large sieve, we use the small sieve (more precisely, the Selberg sieve). Using this naively gives a bound similar to that of Gallagher in (1), as expected. The key advantage of using the small sieve is we can seek improvements if we have good distribution results. After employing Poisson summation our task in then to understand local factors, which turns out to be basically 

\displaystyle \widehat{\mu_p}(\xi_{n-1} , \ldots , \xi_0) =,\ \ \ \ \

\displaystyle = \frac{1}{p^{n}}\sum_{a_0 , \ldots , a_{n-1} \in \mathbb{F}_p} \mu(x^n + a_{n-1} x^{n-1} + \ldots + a_0) \psi_p(\xi_{n-1} a_{n-1} + \cdots + a_0 \xi_0),\ \ \ \ \ (2)

where {\psi_p : \mathbb{F}_p \rightarrow \mathbb{F}_p} is an additive character. This is morally the same problem as estimating  \displaystyle \sum_{n \leq x} \mu(n) e(nx), where {\mu} is the classical Mobius function. Baker and Harman showed that GRH implies that for any {\epsilon > 0}

\displaystyle |\sum_{n \leq x} \mu(n) e(nx)| \ll x^{3/4 + \epsilon}.

As the Riemann hypothesis is known in the function field setting, such a bound should also hold for (2). Indeed Porritt showed this is the case and this allows us to establish Theorem 1.

Effective results on the size and structure of sumsets

For a set {A \subset \mathbb{Z}^d} we define the {N}-fold sumset of {A} via

\displaystyle NA : = \{a_1 + \ldots + a_N : a_i \in A\}.

We have the following result of Khovanskii from 1992.

Theorem 1 (Polynomial size of iterated sumsets): Let {A \subset \mathbb{Z}^d} of size {n}. Then there exists a {N_A \geq 1} and {c_0 , \ldots , c_d \in \mathbb{Q}} such that for {N \geq N_A}

\displaystyle |NA| = \sum_{j=0}^d c_j N^j. \ \ \ \spadesuit

One part of a recent work, Andrew Granville, Aled Walker, and I were able to provide effective bounds for {N_A}. We let

\displaystyle w(A) = \max_{a , b \in A} ||a-b||_{\infty}.

Theorem 2 (Effective Khovanskii): Let {A \subset \mathbb{Z}^d} of size {n}. Then there exists {c_0 , \ldots , c_d \in \mathbb{Q}} such that for any

\displaystyle N \geq 2^{2n} n^{n^3 + 1} w(A)^{n^3}.\ \ \ \ \ (1)

one has

\displaystyle |NA| = \sum_{j=0}^d c_j N^j. \ \ \ \spadesuit

It is not clear that (1) is best possible. Certainly there has to be some dependence on how {A} lies in space, as can be seen by taking the elements of {A} to be increasingly spread out. Curran and Goldmakher showed that when {|A| = d+2}, one needs {N_A} at least the order of { \omega(A)^d }

We first recall an elegant proof of Theorem 1 due to Nathanson and Ruzsa, which is also the starting point for Theorem 2.

Proof of Theorem 1: We consider {\mathbb{Z}_{\geq 0}^n}, equipped with {\leq}, the lexicographical ordering. Let {A = \{a_1 ,\ldots , a_n\}}. We then have a map

\displaystyle \phi : \mathbb{Z}_{\geq 0}^n \rightarrow \bigcup_{j=0}^{\infty} jA,


\displaystyle \phi(x_1 , \ldots , x_n) =x_1 a_1 + \ldots + x_n a_n.

It is worth noting that if {\phi} is injective, we immediately deduce Theorem 1 by the stars and bars. Typically {\phi} is not injective, but it turns out to not be a significant problem. We let {U} be the set of elements {x} such that there exists a {y} with {||y||_1 = ||x||_1}, {y < x}, and {\phi(y) = \phi(x)}. We call any element of {U} useless. They are deemed useless as

\displaystyle |NA| = \{x \in \mathbb{Z}_{\geq 0}^n : ||x||_1 = N\} \setminus U.

There is nothing intrinsic that makes elements of {U} useless, rather it is a consequence of our choice of lexicographical ordering. One can check that {U} is closed under translations from elements of {\mathbb{Z}_{\geq 0}^n}.

We need a way to count the elements of {U} and thus propose another definition. We let {\leq_{{\rm unif}}} be the partial ordering where {x \leq_{{\rm unif}} y} if {x} is smaller than {y} coordinate-wise. Let {U_{\min}} be the elements {x \in U} such that there is no {y\in U} with {y <_{{\rm unif}} x}. Dickson’s lemma implies that {U_{\min}} is finite. For a set {U'}, we let

\displaystyle B(N, U') = \{x \in \mathbb{Z}_{\geq 0 }^n: ||x||_1 = N , x >_{{\rm unif}} u, \ \text{for all} \ u \in U'\}.

Thus we have, by the inclusion-exclusion principle,

\displaystyle | \{x \in \mathbb{Z}_{\geq 0}^n : ||x||_1 = N\} \setminus U| = \sum_{U' \subset U_{\min}} (-1)^{|U'|}| B(N , U')|.

Thus it is enough to show for any finite {U'}, {\#B(N,U')} is polynomial in {N}, for {N} large enough. This follows from the same stars and bars argument mentioned above, as long as

\displaystyle N \geq \max_{u \in U'} ||u||_{\infty}, \ \ \ \ \ (2)

and Theorem 1 follows. {\spadesuit}

Note that this proof does not give any effective bound on {N_A}, as we do not have any control over the set {U_{\min}}. In particular, one would like to have a bound on the {\ell^{\infty}} norm of the elements of {U_{\min}}, in light of (2). In general, one cannot make Dickson’s lemma quantitative, but in our case we can use the structure of {U_{\min}} to do so. The point is that {U} is defined by linear relations, so one can appeal to Siegel’s lemma.

Proof (sketch) of Theorem 2: We translate {A} so that {0 \in A}, which does not effect the problem (in particular, {w(A)} remains unchanged). We build upon the proof of Theorem 1. Suppose {x \in U_{\min}}. As {x \in U}, there is a {y \in \mathbb{Z}_{\geq 0}^n} such that {||x||_1 = ||y||_1}, {y < x}, and {\phi(x) = \phi(y)}. Thus

\displaystyle \sum_{i \in I} x_i a_i = \sum_{j \in J} y_i a_j.

As {x \in U_{\min}}, one can check that {I\cap J = \emptyset}. We now construct a matrix {M} as follows. The first row has {\#I} 1’s followed by {\#J} {-1}‘s. The remaining {d} rows are give by {(a_i)_{i \in I}} and {(-a_j)_{j \in J}}. Thus

\displaystyle M (x,y)^T = 0 \ \ \ \ \ (3)

One would hope to apply Siegel’s lemma, which asserts that (3) has a solution such that {||(x,y)||_{\infty}} is small. The primary issue is that this small solution, {x^*} may have nothing to do with {(x,y)^T}. However one can translate {(x,y)^T} by a multiple of {x^*} to create a solution that is small in a single coordinate. Then one “forgets” this coordinate and tries proceeds by induction. A secondary issue is that the {x^* \in \mathbb{Z}^n}, may have negative coordinates, but this turns out to not be a critical issue. All of this carried out in section 6 of the aforementioned paper with Granville and Walker. {\spadesuit}

Kuratowski Closure and Topological Spaces

This post was the result of conversations with Chris Gartland (from several years ago). In what follows we outline an alternate definition of a topological space due to Kuratowski.

We recall Kuratowski’s closure axioms for a set {X}. These are a set of axioms which can be used to define a topology on a set. Here, one replaces the notion of open (or closed) sets with a closure operation. The closure operation has the advantage of being defined on all subsets of {X}. For{A \subset X}, one may think of the closure of {A}, denoted {\overline{A}}, to be points that are “close to” {A}.

First we recall what is meant by a topological space. We let {\mathcal{P}(X)} be the power set of {X}, that is the set of all subsets of {X}.

{\ } Definition 1: Let {X} be a set and {T \subset \mathcal{P}(X)}. Then {(X,\mathcal{T})} is a topology with open sets {\mathcal{T}} if

  • (a) {\emptyset , X \in \mathcal{T}},
  • (b) if {A, B \in \mathcal{T}}, then {A \cap B \in \mathcal{T}},
  • (c) if {A_i \in \mathcal{T}} for all {i \in \mathcal{I}}, then {\cup_{i\in \mathcal{I}} A_i \in \mathcal{T}}.

Furthermore, we say

\displaystyle \mathcal{C} := \{X \setminus A : A \in \mathcal{T}\},

are the closed sets of {X}. {\spadesuit} {\ }

Note we could have defined (a), (b), (c) in terms of closed sets, utilizing DeMorgan’s law. We make use of this implicitly below. We now present the definition of Kuratowski closure.

{\ } Definition 2: Let {X} be a set and {\overline{\cdot} : \mathcal{P}(X) \rightarrow \mathcal{P}(X)}. We say {\overline{\cdot}} is a closure operation if

  • (i) {\overline{\emptyset} = \emptyset},
  • (ii) if {A \subset X}, then {A \subset \overline{A}},
  • (iii) if {D = \overline{A}}, then {\overline{D} = D},
  • (iv) if {A,B \subset X}, then {\overline{A\cup B} = \overline{A} \cup \overline{B}}. {\spadesuit}

{\ }

As mentioned above, one can informally think of {\overline{A}} as the set of points in {X} which are “close” to {A}. We now explain that these two definitions are equivalent. To define a closure operation from Definition 1, we set

\displaystyle \overline{A} := \bigcap_{A \subset C \ \text{closed}} C.\ \ \ \ \ (1)

Note that {\overline{A}} is closed by (c). Given a closure operation, we may define the closed sets to be

\displaystyle \mathcal{C} : = \{\overline{A} : A \subset X\}.\ \ \ \ \ (2)

{\ } Proposition 1: Let {(X, \mathcal{T})} be a topological space as in Definition 1. Then the operation defined in (1) is a closure operation in the sense of Definition 2. {\spadesuit} {\ }

Proof: Let

\displaystyle \mathcal{C} = \{X \setminus A : A \in \mathcal{T}\},

be the set of closed sets in {X}. Let {A , B \subset X}. By (1), we have {A \subset \overline{A}} and thus (ii) holds. As {X} is open, {\emptyset \in \mathcal{C}} and so (i) is satisfied. Note for any closed set, {C \in \mathcal{C}}, we have {\overline{C} = C} by (ii) and that {C} itself appears on the right hand side of (1). By (c), we have that

\displaystyle D = \bigcap_{A \subset C \ \text{closed}} C \in \mathcal{C},

and so {\overline{D} = D}. This proves (iii) and so it remains to show (iv). Note that {\overline{A} \cup \overline{B}} is a closed set containing {A \cup B}, by (b). Thus

\displaystyle \overline{A \cup B} \subset \overline{A} \cup \overline{B}.\ \ \ \ \ (3)

Now let {C \in \mathcal{C}} that contains {A \cup B}. Then {A, B \subset C} and so

\displaystyle A , B \subset \ \bigcap_{(A\cup B) \subset C \ \text{closed}} C.

This proves the reverse inclusion of (3) and thus of Proposition 1. {\spadesuit}

We now show that a closure axiom can be used to define a topology.

{\ } Proposition 2: Let {\overline{\cdot} : \mathcal{P}(X) \rightarrow \mathcal{P}(X)} be a closure axiom as in Definition 2. Then the sets defined in (2) form the closed sets of a topology in the sense of Definition 1. {\spadesuit} {\ }

Proof: We implicitly make use of DeMorgan’s law to transition bewtween closed and open sets. By {(i)}, we have {\emptyset \in \mathcal{C}} and by (ii), we have {X \in \mathcal{C}}. Thus {(a)} is established. Let {A, B \in \mathcal{C}}. By (iii), (iv) and (2), we have

\displaystyle A \cup B = \overline{A \cup B} \in \mathcal{C},

which establishes (b). Let {C_i \in \mathcal{C}} for all {i \in \mathcal{I}} some index set. Then we have

\displaystyle \overline{\cap_{i \in \mathcal{I}} C_i}= \cap_{i \in \mathcal{I}} C_i.

Indeed, the backwards inclusion follows from (ii). To see the forward direction, it is enough to show for any {i \in \mathcal{I}},

\displaystyle \overline{\cap_{i \in \mathcal{I}} C_i} \subset \overline{C_i} = C_i.

The second equality is (iii). The first subset inequality follows from (iv) as if {A \subset B} then

\displaystyle \overline{A} \subset \overline{A} \cup \overline{B} = \overline{A \cup B} = \overline{B}.

We apply this with {A = \cup_{i\in \mathcal{I}} C_i} and {B = C_i}. {\spadesuit}

Recall if {(X , \mathcal{T}_X)} and {(Y , \mathcal{T}_Y)} are topological spaces, then

\displaystyle f : X \rightarrow Y,

is said to be continuous if the pre-image of every open set in {Y} is open in {X}, that is

\displaystyle f^{-1}(B) \in \mathcal{T}_X,\ \ \ \ \ (4)

for all open {Y}. As {f^{-1}(Y \setminus B) = X \setminus f^{-1}(B)}, (5) is equivalent to the analogous definition for closed sets. It turns out that we may equivalently define a map to be continuous if

\displaystyle f(\overline{A}) \subset \overline{f(A)}.\ \ \ \ \ (5)

This can be informally interpreted as points that are close to {A} are mapped to points that are close to {f(A)}. We now prove this.

{\ } Proposition 3: Let {f : X \rightarrow Y} be a function. Then {f} is continuous in the sense of (4) if and only if it is continuous in the sense of (5). {\spadesuit} {\ }

Proof: We start with the forward direction. Let {A \subset X}. Then by (4), {f^{-1}(\overline{f(A)})} is closed. As {f(A) \subset \overline{f(A)}}, we have

\displaystyle A \subset f^{-1}(\overline{f(A)}).

Since the right hand side is closed, by (ii) and (iii), we have

\displaystyle \overline{A} \subset f^{-1}(\overline{f(A)}).

Applying {f} to both sides establishes (5).

Now we show the reverse implication. Let {B} be a closed set. Then by (5),

\displaystyle f(\overline{f^{-1}(B)}) \subset \overline{f(f^{-1}(B))} =\overline{B} = B .

As any element which is mapped to {B} by {f} must lie in {f^{-1}(B)}, we have

\displaystyle \overline{f^{-1}(B)} \subset f^{-1}(B).

By (ii), equality holds and thus {f^{-1}(B)} is closed. {\spadesuit}

Exponential Sums along Oblique Lines

Thanks to Changhao Chen, Burak Erdońüan, and Igor Shparlinski for useful discussions surrounding this post.

Let {k} be a positive integer (which we take later to be {\geq 11}). We consider the exponential sum

\displaystyle S(x,y) : = \sum_{n=N}^{2N} e(xn + yn^k),\ \ \ \ \ (1)

where {e(x) : = e^{2\pi i x}}. We are interested in bounds for

\displaystyle \sup_{(x,y) \in \mathcal{L}_z} |S(x,y)|,

where {\mathcal{L}_z \subset [0,1)^2} are a family of sets indexed by some parameter {z\in \mathbb{R}}. For simplicity, we only consider, for fixed {(a,b) \in \mathbb{R}},

\displaystyle \mathcal{L}_z = \{(x,y) \in [0,1)^2 : ax + by = z\}.

We would like to show that for most {z}, (1) is small. This is supported by the usual heuristic that we expect square root cancellation in (1). On the other hand, {S(x,y)} is large for some special values of {(x,y)} (say {(0,0)}), so it certainly matters how the {\mathcal{L}_z} lie in space. Burak Erdońüan and I studied these types of questions (also the topic of this previous blog post) motivated from understanding the fractal dimension of solutions to certain PDE (see the introduction and references within for a complete history). We let

\displaystyle s(k) = k(k-1) + 2 \lfloor \sqrt{2k-2}\rfloor - 2.

{\ } Theorem 1 (Chen-Shparlinski): Let {\epsilon > 0} and

\displaystyle \alpha > 1 - \frac{1}{1 + s(k)}.

Then for a.e. {z \in \mathbb{R}}, with respect to the Lebesgue measure,

\displaystyle \sup_{(x,y) \in \mathcal{L}_z} S(x,y) \ll_z N^{\alpha + \epsilon}, \ \ \ \spadesuit

{\ }

Let us make some remarks before beginning the proof. It is worth noting that for very small {k} we know the best value of {\alpha}. Indeed, Brandes, Parsell, Poulias, Vaughan, and I showed {\alpha = 3/4} is admissible and cannot be improved. In the aforementioned paper of Erdońüan, we mentioned that one could obtain a variant of Theorem 1 by invoking Vinogradov’s mean value theorem, which is a best possible bound for

\displaystyle \int_{[0,1]^k} |\sum_{n=N}^{2N} e(x_1 n + x_2 n^2 + \cdots + x_k n^k)|^{p}.\ \ \ \ \ (2)

The guiding principle is that if an exponential sum is large at a single point, then one can create many other points where the exponential sum is large. On the other hand, there cannot be too many points where this occur as (2) is small. This is a somewhat unsatisfactory approach, as it is not clear that the {k} variable mean value in (2) is the right tool to analyze the two variable {S(x,y)}. Recently, Chen and Shparlinski instead utilized the following two variable mean value type theorem of Wooley, which turns out to improve the bounds a bit and simplify the proof.

{\ } Theorem 2 (Wooley): Suppose {k \geq 11} is an integer. Then for any {\sigma \geq s(k)}

\displaystyle \int_0^1 \int_0^1 |S(x,y)|^{\sigma} dx dy \leq N^{\sigma - k - 1 + o(1)}. \ \ \spadesuit

{\ }

Note that Theorem 2 is best possible, in a certain sense. By considering a small {N^{-1} \times N^{-k}} rectangle near {(0,0)}, we see

\displaystyle \int_0^1 \int_0^1 |S(x,y)|^{\sigma} dx dy \gg N^{\sigma - k - 1}.

Thus Theorem 2 cannot be improved much, for the values of {\sigma} for which it applies. It is not clear that the range of {\sigma} is best possible. A natural conjecture is that Theorem 2 holds for

\displaystyle \sigma > 2(k+1) .

Such an improvement would improve Theorem 1.

Proof of Theorem 1: We only prove for {N=2^j}, for simplicity. The reader may consult Chen and Shparlinski’s paper for the general case, where the additional idea is to employ the completion technique.

Let {\epsilon > 0} and {0 < \alpha < 1}. We partition {[0,1)^2} into a grid of {O(N^{2 \alpha - k - 3 - 2 \epsilon})} small rectangles of size approximately

\displaystyle N^{\alpha - 2 - \epsilon} \times N^{\alpha - k - 1 - \epsilon}.

We label these rectangles by

\displaystyle \cup_{R \in \mathcal{R}} R.

The point is that (1) is does not change much on such rectangles. Indeed it is easy to check, using {e(x) = 1 + O(x)}, that (for {N} large enough) if

\displaystyle |S(x,y)| \geq N^{\alpha},

for some {(x,y) \in R}, then

\displaystyle |S(x',y')| \geq N^{\alpha}/2,

for any {(x',y') \in R}. We let {\mathcal{R}_{\alpha} \subset \mathcal{R}} consist of the rectangles {R} such that there is a {(x,y) \in \mathcal{R}} with {|S(x,y)| \geq N^{\alpha}}. Combining this with the mean value estimate in Theorem 2, we see that {\#\mathcal{R}_{\alpha}} cannot be too large.

Indeed, Markov’s inequality and Theorem 2, we see that for {\sigma \geq s(k)},

\displaystyle N^{2 \alpha -2 -k - 1 - 2 \epsilon} N^{\alpha \sigma} \#\mathcal{R}_{\alpha} \leq \int_0^1 \int_0^1 |S(x,y)|^{\sigma} dx dy \leq N^{\sigma - k - 1 + o(1)}.


\displaystyle \#\mathcal{R}_{\alpha} \leq N^{(1-\alpha)\sigma -2\alpha+2 + 2 \epsilon + o(1)}.\ \ \ \ \ (3)

We now consider the image of these rectangles under the map

\displaystyle (x,y) \mapsto ax + by.

We have

\displaystyle \{z\in \mathbb{R} : |S(x,y)| \geq N^{\alpha}, \ \ \text{for some} \ (x,y) \in \mathcal{L}_z\} \subset f\left(\bigcup_{R \in \mathcal{R}_{\alpha}}R\right) = \bigcup_{R \in \mathcal{R}_{\alpha}}f(R),


\displaystyle f(x,y) = ax+by.

Note that {f} does not distort rectangles too much, so that

\displaystyle \lambda(f(R)) \ll_{a,b} N^{\alpha - 2 + \epsilon}.

where {\lambda} is the Lebesgue measure. Thus by subadditivity of the Lebesgue measure and (3)

\displaystyle \lambda(\{z\in \mathbb{R} : |S(x,y)| \geq N^{\alpha}, \ \ \text{for some} \ (x,y) \in \mathcal{L}_z\} )\ll_{a,b} N^{\alpha - 2 +\epsilon} N^{(1-\alpha)\sigma -2\alpha+2 + 2 \epsilon + o(1)}.

Note here that {N} is fixed. What we actually care about is what happens for a fixed {z} and {N \geq N(z)} for some large {N(z)}. There is a standard trick from probability (or analysis) to apply the Borel-Cantelli lemma. We first apply the above result with {N = 2^j} to find

\displaystyle \lambda(\{z\in \mathbb{R} : |S(x,y)| \geq 2^{j\alpha}, \ \ \text{for some} \ (x,y) \in \mathcal{L}_z\} ) \leq 2^{j((1-\alpha)\sigma - \alpha + 2 \epsilon )}.\ \ \ \ \ (4)

By the Borel-Cantelli lemma, if

\displaystyle \sum_{j=1}^{\infty} 2^{j((1-\alpha)\sigma - \alpha + 2 \epsilon )}< \infty,

then the set of {z} such that (4) holds for infinitely many {j} has measure zero. This is implied by

\displaystyle (1-\alpha)\sigma - \alpha < 0,

as long as {\epsilon} is sufficiently small. This, in turn, is implied by

\displaystyle \alpha > \frac{\sigma}{\sigma + 1} = 1 - \frac{1}{\sigma + 1} . \ \ \ \spadesuit

A large gap in a dilate of a set

I recently uploaded “a large gap in a dilate of a set,” to the arXiv. We prove the following.

{\ } Theorem 1: Let {A \subset \mathbb{F}_p} with at least two elements. Suppose {N \leq 2p/|A| -2}. Then there is a {x \in \mathbb{F}_p} and {d \in \mathbb{F}_p^{\times}} such that

\displaystyle (d\cdot A + x ) \cap \{1 , \ldots , N\} = \emptyset.  \ \ \ \spadesuit

{\ }

As the note is only 3 pages, we do not remark on the proof (which uses the polynomial method) but elaborate on some related topics. Note by the pigeon-hole principle, Theorem 1 is true for every {d} if we only insist {N \geq p/|A| - 1}. Thus we have effectively doubled the bound coming from the pigeon hole principle. Note without dilation, Theorem 1 is false, as one can take {|A|} equally spaced elements.

One can ask what happens for Theorem 1 if one does not allow translation by {x}. It turns out that one cannot hope to go beyond {N \geq 2p/|A|}, as it was shown in this paper of Chen, Shparlinski and Winterhof, using the example

\displaystyle A = \{1 , \ldots , p/N\} \cup -\{1 , \ldots , p/N\} .

It is an interesting question to decide to what extent Theorem 1 is true with translation by {x}. We remark this is in a similar spirit to the lonely runner conjecture.

It would be nice if Theorem 1 were true for {N \geq C p/|A|} for all {C}, even in the special case when {|A| \sim \sqrt{p}}. Certainly this is true for a random set, without the need for dilation.

In particular, this would give us hope in answering an old question of Erdös, which we recall. A Sidon set in a abelian group is a set such that {a+b = c+d} with {a,b,c,d \in A} implies {\{a,b\} = \{c,d\}}. Let {r_2(N)} be the largest Sidon set contained in {\{1 , \ldots , N\}}. Erdös asked if

\displaystyle r_2(N) = N^{1/2} + O(1).

There are constructions of Sidon sets of size {N^{1/2}} (for some {N}) coming from { \mathbb{Z} /N \mathbb{Z}} for well-chosen {N}. The hope would be to dilate the set in {\mathbb{Z}/N \mathbb{Z}} so there is a large gap of size {g}, thus finding a Sidon set of inside of {\{1 , \ldots , N-g\}} in place of {\{1 , \ldots , N\}}. It is actually not known if we can take {N } to be a prime in the modular construction, which may be found in this nice survey of O’ Bryant. This is certainly a question of interest.

On the other hand, one can hope to improve Theorem 1 for some of these constructions. It turns out one can easily check that Ruzsa’s construction which is the set {A \subset \mathbb{Z}/ (p(p-1)) \mathbb{Z}} does not admit large gaps. Indeed the set has size {\sim p} but any dilate of {A} does not contain a gap significantly longer than {2p}. This also shows Theorem 1 is false for general cyclic groups. The point is that in his construction the natural projection to {\mathbb{Z}/(p-1)\mathbb{Z}} maps {A} surjectively.

This seems to be a bit of a red herring in the application to Sidon sets. On the other hand, for the well-known construction of Bose-Chowla (contained in the aforementioned survey), the analog of Theorem 1 is true and there is no reason to suspect that it cannot be improved. In fact, in this case a proof also proceeds by the polynomial method.

An Analytic Approach to the Cardinality of Sumsets

D√°vid Matolcsi,¬†Imre Ruzsa,¬†Dmitrii Zhelezov¬†and I just uploaded our paper, “An analytic approach to cardinalities of sumsets” to the arXiv. Alongside¬†Ben Green, we also uploaded a follow up paper, “A weighted Pr√©kopa-Leindler inequality and sumsets with quasi-cubes.”

Our focus is sumsets of finite subsets of {\mathbb{Z}^d}. For instance, if {A \subset \mathbb{Z}} and {d} is a positive integer, we have 

\displaystyle |A^d + A^d| =|A+A|^d.

If {A} is not an arithmetic progression, it is known that 

\displaystyle |A+A| \geq 2|A|,

and so we obtain 

\displaystyle |A^d + A^d| \geq 2^d |A|.\ \ \ \ \ (1)

It is natural to look to obtain analogs of (1) for more general subsets of {\mathbb{Z}^d}. For instance, the Brunn-Minkowski inequality implies the continuous analog, 

\displaystyle \mu(X+X) \geq 2^d \mu(X),

whenever {X} is a compact subset of {\mathbb{R}^d}. In the discrete setting such a result is not true. First of all, the notion of cardinality does not distinguish dimension. Thus we can take a one dimensional arithmetic progression and place in {\mathbb{Z}^d}, which will not obtain the growth in (1). For a legitimate {d} dimensional set, one can take an arithmetic progression alongside {d-1} random points and {|A+A| / |A|} will grow only linearly in {d}. There are some situations when we can establish (1). For instance, if {A} contains {\{0,1\}^d}, then Green and Tao showed

\displaystyle |A+A| \geq 2^{d/2} |A|.

Freiman (see chapter 5 of Tao and Vu) also showed if {A \subset \mathbb{Z}^d} such that every hyperplane intersects {A} in {\ll_{d, \epsilon} |A|} points, then 

\displaystyle |A+A| \geq (2^d - \epsilon) |A|.

We also mention the work of Gardner and Gronchi, who prove inequalities for general {d}-dimensional sets. The drawback there is that the extremal examples are nearly one dimensional, and particular they only derive growth that is linear in the dimension. We provide a result in a similar spirt to Green and Tao. To state our result, we need a definition. We define a quasi-cube inductively (on the dimension). Any two point set is a 1-dimensional quasi-cube. A {d} dimensional set {U} is a quasi-cube if {U = U_0 \cup U_1} where {U_0 ,U_1}, where {U_j = (x_j + L_j) \cap U}, with {x_j \in \mathbb{Z}^d}, {L_j} a hyperplane and {U_0,U_1} are themselves quasi-cubes. A cube is a quasi-cube. Also, the trapezoid: 

\displaystyle U = \{(0,0) , (1,0), (0,1) , (1,x)\} , \ \ \ \text{with} \ \ x \neq 0,

is a quasi-cube.

{\ } Theorem 1: Let {A \subset \mathbb{Z}^d} be finite. Suppose that A contains U which a subset of a quasi-cube. Then 

\displaystyle |A+A| \geq |U|^{1/2} |A|, \ \ \ \spadesuit.{\ }

This has applications to the sum-product problem via the Bourgain-Chang argument and will be explored in a future paper of Pálvölgyi and Zhelezov. We discuss some of the ideas. As mentioned above, Theorem 1 can be thought of as a discrete analog of the Brunn-Minkowski inequality. The Brunn-Minksowski inequality can be proved using the Prékopa-Leindler inequality, and this is the viewpoint we adopt. 

To start, for {A,B \subset \mathbb{Z}}, we have 

\displaystyle |A+B| \geq |A| + |B| - 1,\ \ \ \ \ (2)

and for {X,Y \subset \mathbb{R}^d} are compact, we have 

\displaystyle \mu(X+Y) \geq \mu(X) + \mu(Y).\ \ \ \ \ (3)

Both can be proved in the same way: by finding a translate of {X} and a translate of {Y} in {X+Y} that are almost disjoint. In the continuous setting (3) is used to establish a functional analog. For compactly support and bounded {f,g:\mathbb{R} \rightarrow \mathbb{R}_{\geq 0}} we define

\displaystyle f \mathbin{\overline *} g(z) : = \sup_{x+y =z} f(x) g(y).

Then the Prékopa-Leindler inequality implies that

\displaystyle \int f \mathbin{\overline *} g \geq ||f||_2 ||g||_2.\ \ \ \ \ (4)

When {f=1_X} and {g=1_Y}, we obtain a weaker variant of (3):

\displaystyle \mu(X+Y) \geq 2 \mu(X)^{1/2} \mu(Y)^{1/2}.

To go in the other direction, one applies¬†(3)¬†to the level sets of {f} and {g}.¬†Gardner’s survey¬†provides more accurate implications along these lines. Thus it is natural to ask for a functional analog of¬†(2). Indeed, we let {a,b: \mathbb{Z} \rightarrow \mathbb{R}_{\geq 0}} be compactly supported. Then Pr√©kopa showed that

\displaystyle ||a \mathbin{\overline *} b|| \geq 2 ||a||_2 ||b||_2 - 1.\ \ \ \ \ (5)

We invite the reader to try to discover a proof, it is rather non-trivial! In any case, the next step in Prékopa-Leindler is to tensorize, as is explained in this blog post of Tao or section 4 of the aforementioned survey of Gardner. The point is the integral inequality (4) can be obtained by induction on the dimension by applying the lower dimensional analog of (4) to functions such as {f(x_1 , \ldots , x_{d-1} , x_d)} with {x_d} fixed. Doing this, one obtains, for compactly support and bounded {f,g:\mathbb{R} \rightarrow \mathbb{R}_{\geq 0}} 

\displaystyle \int f \mathbin{\overline *} g \geq ||f||_2 ||g||_2.

A slight generalization of this inequality quickly implies the Brunn-Minkowski inequality. In the discrete setting, the {-1} present in both (2) and (5) are quite a nuisance, particularly in the tensorization step. To get around this, we observe that 

\displaystyle |A+B+U| \geq |A|+|B| \geq 2 |A|^{1/2} |B|^{1/2},

{U \subset \mathbb{Z}} of size 2. It turns out one has 

\displaystyle ||a \mathbin{\overline *} b \mathbin{\overline *} 1_U|| \geq 2 ||a||_2 ||b||_2 ,

as was originally observed by Prékopa himself. One has an easier time tensorizing this, and following this one can obtain 

\displaystyle |A+B+\{0,1\}^d| \geq |A|^{1/2} |B|^{1/2}.

In our work, we take an abstract approach, defining

\displaystyle \beta(U) = \inf_{A,B \neq \emptyset} \frac{|A+B+U|}{|A|^{1/2} |B|^{1/2}}.

Note that {\beta(U) \leq |U+U+U|/|U|}. Indeed, {\beta} is intended to be a replacement of the usual notion of the doubling constant, {|U+U|/|U|}. It turns out for certain large dimensional sets, we can accurately estimate {\beta}. For instance, we show that if {U} is a subset of a quasi-cube then

\displaystyle \beta(U) = |U|,

which quickly implies Theorem 1. The upper bound follows immediately from the definition of {\beta}, it is the lower bound that takes some work. To do this, we show that this is the same as a functional analog and that tensorization occurs in general. We also explore general properties (for instance {\beta} is independent of the ambient group) and present a bunch of conjectures (which may be interpreted as things we were unable to prove or disprove). 

The above outline nearly works for subsets of quasi-cubes, though it turns out one needs a stronger 1 dimensional inequality of the form

\displaystyle ||f \mathbin{\overline *} g \mathbin{\overline *} h_{\delta}||_1 \geq (1+\delta) ||f||_2 ||g||_2,

where {h = (1,\delta)}. The cases {\delta = 0,1} were already known but for other values of {\delta} it is tricker. There are now two proofs: in the original paper we use a variational argument while in the follow up paper we derive it from the Prékopa-Leindler inequality.

This is a sort of “tensorization plus two point inequality argument,” which is present in other works (i.e.¬†Beckner’s inequality).

The Frobenius Postage Stamp Problem, and Beyond

Andrew Granville and I just uploaded, “The Frobenius postage stamp problem, and beyond” to the arXiv.

Let {A \subset \mathbb{Z}} with {\min A = 0}, {\max A = b}, and who has greatest common divisor of all the elements is 1. For positive integer {N}, we are interested in the structure of

\displaystyle NA = \{a_1 + \ldots + a_N : a_i \in A\}.

For instance,

\displaystyle NA \subset \{0, \ldots ,bN\}.\ \ \ \ \ (1)

Equality does not hold in general. For instance if {1 \notin A}, then {1 \not NA} for any {N \geq 1}. We set

\displaystyle \mathcal E(A) = \mathbb{Z}_{\geq 0} \setminus \cup_{M \in \mathbb{Z}_{\geq 1}} MA ,

Thus we can refine (1) to

\displaystyle NA \subset \{0 , \ldots , bN\} \setminus \mathcal E (A)\ \ \ \ \ (2)

It turns out there is one more obstruction. Note that {n \in NA} if and only if {bN-n \in bN- NA} and so

\displaystyle bN -n \notin \mathcal E(b-A).

Thus we may refine (2) to

\displaystyle NA \subset \{0 , \ldots , bN\} \setminus (\mathcal E (A) \cup (bN - \mathcal E(b-A))).\ \ \ \ \ (3)

Our main result is the following.

{\ } Theorem 1: For {N \geq b}, (3) is an equality. {\spadesuit} {\ }

Theorem 1 is close to best possible, as can be seen from {A = \{0,1,b-1,b\}}. For 3 element sets, we actually show Theorem 1 holds for all {N \geq 1}, which contains some of the ideas present in the general case.

We prove Theorem.1 for {N\geq 2b} from first principles. To get from {2b} to {b}, here are two key additional inputs: a structural result of Savchev and Chen and Kneser’s theorem from additive combinatorics.

A key definition is {n_{a,A}}, which is the smallest element in {\mathcal P (A) : = \cup_{M \in \mathbb{Z}_{\geq 1}} MA} that is equivalent to {a \ ({\rm Mod}) \ b}. For instance, it follows from a short argument of Erdös that {n_{a,A} \in bA}. Indeed we have

\displaystyle n_{a,A} = \sum_{j=1}^r a_j.

If any subsum is {0 \ ({\rm Mod}) \ b}, we may remove it to get a smaller element in {\mathcal{P}(A)} that is {a \ ({\rm Mod}) \ b}. Thus no subsum is {0 \ ({\rm Mod}) \  b}. But this quickly implies that {r \leq b}, as desired.

We also study a natural higher dimensional analog of Theorem 1, utilizing some basic tools such as Carath√©odory’s Theorem and Mann’s Lemma. In this setting, we provide an analog of Theorem 1, though our bounds are not nearly as good.

On generating functions in additive number theory, II: Lower-order terms and applications to PDEs

Julia Brandes,¬†Scott Parsell,¬†Konstantinos Poulias,¬†Bob Vaughan, and I recently upload our paper “On generating functions in additive number theory, II: lower-order terms and applications to PDEs,” to the arXiv. This is somewhat of a sequel to¬†earlier work¬†of Vaughan as well as a recent paper of Burak¬†Erdogan and myself¬†(see also¬†this previous blog post). This work began during the¬†Heilbronn Decoupling and Efficient Congruencing workshop¬†hosted by¬†Kevin Hughes¬†and¬†Trevor Wooley.

In what follows, we adopt Vinogradov’s notation and thus write {a \ll b} when {a = O(b)}. Let {t} and {x} be real numbers. We are interested in the exponential sum 

\displaystyle S(t,x) := \sum_{1 \leq n \leq N} e(tn^3 + nx) , \ \ \ e(\theta) : = e^{2 \pi i \theta}.\ \ \ \ \ (1)

We could replace the exponent 3 by any other integer exponent, but we choose 3 for simplicity, as well as being the case of most interest. Weyl obtained, using what is now known as Weyl differencing, estimates for (1), showing that for almost all {t}, and every {\epsilon >0}, 

\displaystyle \sum_{1 \leq n \leq N} e(tn^2 + nx) \ll N^{1/2+\epsilon} , \ \ \ \sum_{1 \leq n \leq N} e(tn^3 + nx) \ll N^{3/4 + \epsilon}.\ \ \ \ \ (2)

The quadratic estimate is best possible, as is seen from Parseval. We expect a matching estimate for the cubic exponential sum in (2), but (2) is still the best that is known today. The current project originally started in an attempt to understand better three different proofs of (2). We suppose 

\displaystyle t = a/q + \beta_t , \ \ \ |\beta_t| \leq \frac{1}{q^2}, \ \ \ (a,q) = 1,\ \ \ \ \ (3)

and then choose {j} so that 

\displaystyle |x - j/q| \leq 1/(2q).\ \ \ \ \ (4)

By uncertainty principle heuristics, we expect {S}, as defined in (1), to be constant on scales 

\displaystyle |t-a/q| \leq \frac{1}{N^3} , \ \ \ |x-j/q| \leq \frac{1}{N}.

On the other hand {S(a/q , j/q)} is easy to calculate: 

\displaystyle S(a/q , j/q) =\frac{N}{q} \sum_{j=1}^q e(a/q j^3 + nj/q) + O(q).

In the¬†circle method, one needs precise versions of this heuristic. For instance, it is known (see Theorem 7.2 in¬†Vaughan’s book) that¬†

\displaystyle S(t,x) = S(a/q , j/q)\frac{ I(\beta_t , \beta_x)}{N} + \Delta,\ \ \ \ \ (5)


\displaystyle I(\beta_t , \beta_x) : = \int_{1}^N e(\beta_t u^3 + \beta_x u)du.

and the error term satisfies 

\displaystyle \Delta \ll q(1 + |\beta_x| N + |\beta_t |N^3),\ \ \ \ \ (6)

Note that the estimate, (6), becomes nontrivial only if {\beta_x N} and {\beta_t N^3} are much less than 1 as predicted by the uncertainty principle. A proof of (6) follows relatively quickly from summation by parts. One should think of the main term as being of size roughly {N/\sqrt{q}}, in light of Gauss sum estimates. 

Vaughan, Theorem 4.1, asserts the improvement, in the special case {\beta_x = 0}

\displaystyle \Delta \ll q^{1/2 + \epsilon} (1 + N^3 |\beta_t|)^{1/2}.\ \ \ \ \ (7)

It is worth noting that¬†(7)¬†allows for a proof of Weyl’s inequality,¬†(1). Unfortunately the appearance of the {N^3} (as opposed to a {N^2}) seems to prevent one from going beyond Weyl’s inequality (perhaps because one is using a major arc technique to prove a minor arc result). One consequence of our main result below allows for a proof of Weyl inequality in a similar manner with the presence of a nontrivial linear phase.¬†

Br√ľdern and Robert improved¬†(6)¬†to¬†

\displaystyle \Delta \ll q^{2/3 + \epsilon} (1 + |\beta_x| N + |\beta_t |N^3)^{1/2}.\ \ \ \ \ (8)

In the present paper we show that the estimate (7) still holds in the case when {\beta_x \neq 0}, provided one takes into account addition main terms, thus improving (7).

{\ } Theorem 1: Suppose {t} and {x} are as above, as in (3) and (4). Then 

\displaystyle S(t,x) =\sideset{}{^\dag} \sum_{\substack{d | q \\ ([[j/d]] , q/d) = 1}} S(d[[j /d]] / q , a/q) \frac{I(\beta_t, x - d [[j/d]] )}{N} + \Delta,


\displaystyle \Delta \ll q^{1/2 + \epsilon} (1 +\beta_t N^3)^{1/2}.

Here {[[z]]} is the nearest integer to {z} and {\sideset{}{^\dag}\sum} indicates the sum is over distinct values of {d [[j/d]]}. {\spadesuit} {\ }

The key point is that there are multiple main terms in Theorem 1, given in the sum, which allows an improvement of the error term in (7)

Theorem 1 also indicates that one may be able to perform a major arc only analysis of the system of equations 

\displaystyle \sum_{j=1}^4 x_i^k - y_i^k, \ \ \ k = 1,3,

though we do not pursue this in the current paper. We are also able to use Theorem 1 to estimate the following.

{\ } Theorem 2: Let {k = 2} or {k = 3}. For a.e. {c \in \mathbb{R}} and every {\epsilon >0}

\displaystyle \sup_{N \geq 1} \frac{1}{N^{3/4+\epsilon}} \sup_{t} | \sum_{1 \leq n \leq N} e(t n^k + (t+c)n| \ll 1.\ \ \ \ \ (9)

Furthermore (9) is best possible, in the sense that one cannot replace the exponent {3/4} with anything smaller. {\spadesuit} {\ }

One can interpret the condition in (9) as the supremum of {S(t,x)} such that {(t,x)} lies on the diagonal line (on the torus), {u+v = -c}. Naively one would expect square root cancellation in (9), though it turns out this is not the case. The point is that square root cancellation is a heuristic that only applies to minor arcs, while the supremum in (9) is obtained on a major arc. Thus it is natural to utilize estimates that can handle both minor and major arcs simultaneously, such as the one in Theorem 1. Combining Theorem 2 with previous joint work with Burak Erdo\u gan, we can provide bound for the fractal dimension of the real and imaginary parts of the solution to Airy’s equation, 

\displaystyle u_t + u_{xxx} = 0 , \ \ \ u(0,x) = g(x).\ \ \ \ \ (10)

{\ } Corollary 1 (Fractal Dimension of Airy): Let {u(t,x)} be the solution to (10) with {g} a non-constant characteristic function of an interval. Then the maximum fractal dimension of the real and imaginary parts of {u(t,x)} restricted to 

\displaystyle \{(t,x) \in \mathbb{T}^2 : t+x = -c\},

is at most {17/8} for a.e. {c}. {\spadesuit} {\ }

The introduction of the aforementioned joint work contains a detailed motivation and history to this problem. It was also explored a bit in two undergraduate projects, and one can find some pictures they generated in a 2018 project and another in a 2019 project. We finish by noting the bound of {17/8} in Corollary 1 does not match the known (and expected) lower bound of {3/4}.