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A common task in analysis is to obtain bounds on sums

\displaystyle  \sum_{n \in A} f(n)

or integrals

\displaystyle  \int_A f(x)\ dx

where {A} is some simple region (such as an interval) in one or more dimensions, and {f} is an explicit (and elementary) non-negative expression involving one or more variables (such as {n} or {x}, and possibly also some additional parameters. Often, one would be content with an order of magnitude upper bound such as

\displaystyle  \sum_{n \in A} f(n) \ll X

or

\displaystyle  \int_A f(x)\ dx \ll X

where we use {X \ll Y} (or {Y \gg X} or {X = O(Y)}) to denote the bound {|X| \leq CY} for some constant {C}; sometimes one wishes to also obtain the matching lower bound, thus obtaining

\displaystyle  \sum_{n \in A} f(n) \asymp X

or

\displaystyle  \int_A f(x)\ dx \asymp X

where {X \asymp Y} is synonymous with {X \ll Y \ll X}. Finally, one may wish to obtain a more precise bound, such as

\displaystyle  \sum_{n \in A} f(n) = (1+o(1)) X

where {o(1)} is a quantity that goes to zero as the parameters of the problem go to infinity (or some other limit). (For a deeper dive into asymptotic notation in general, see this previous blog post.)

Here are some typical examples of such estimation problems, drawn from recent questions on MathOverflow:

  • (i) (From this question) If {d,p \geq 1} and {a>d/p}, is the expression

    \displaystyle  \sum_{j \in {\bf Z}} 2^{(\frac{d}{p}+1-a)j} \int_0^\infty e^{-2^j s} \frac{s^a}{1+s^{2a}}\ ds

    finite?
  • (ii) (From this question) If {h,m \geq 1}, how can one show that

    \displaystyle  \sum_{d=0}^\infty \frac{2d+1}{2h^2 (1 + \frac{d(d+1)}{h^2}) (1 + \frac{d(d+1)}{h^2m^2})^2} \ll 1 + \log(m^2)?

  • (iii) (From this question) Can one show that

    \displaystyle  \sum_{k=1}^{n-1} \frac{k^{2n-4k-3}(n^2-2nk+2k^2)}{(n-k)^{2n-4k-1}} = (c+o(1)) \sqrt{n}

    as {n \rightarrow \infty} for an explicit constant {c}, and what is this constant?

Compared to other estimation tasks, such as that of controlling oscillatory integrals, exponential sums, singular integrals, or expressions involving one or more unknown functions (that are only known to lie in some function spaces, such as an {L^p} space), high-dimensional geometry (or alternatively, large numbers of random variables), or number-theoretic structures (such as the primes), estimation of sums or integrals of non-negative elementary expressions is a relatively straightforward task, and can be accomplished by a variety of methods. The art of obtaining such estimates is typically not explicitly taught in textbooks, other than through some examples and exercises; it is typically picked up by analysts (or those working in adjacent areas, such as PDE, combinatorics, or theoretical computer science) as graduate students, while they work through their thesis or their first few papers in the subject.

Somewhat in the spirit of this previous post on analysis problem solving strategies, I am going to try here to collect some general principles and techniques that I have found useful for these sorts of problems. As with the previous post, I hope this will be something of a living document, and encourage others to add their own tips or suggestions in the comments.

Read the rest of this entry »

Rachel Greenfeld and I have just uploaded to the arXiv our paper “Undecidability of translational monotilings“. This is a sequel to our previous paper in which we constructed a translational monotiling {A \oplus F = {\bf Z}^d} of a high-dimensional lattice {{\bf Z}^d} (thus the monotile {F} is a finite set and the translates {a+F}, {a \in A} of {F} partition {{\bf Z}^d}) which was aperiodic (there is no way to “repair” this tiling into a periodic tiling {A' \oplus F = {\bf Z}^d}, in which {A'} is now periodic with respect to a finite index subgroup of {{\bf Z}^d}). This disproved the periodic tiling conjecture of Stein, Grunbaum-Shephard and Lagarias-Wang, which asserted that such aperiodic translational monotilings do not exist. (Compare with the “hat monotile“, which is a recently discovered aperiodic isometric monotile for of {{\bf R}^2}, where one is now allowed to use rotations and reflections as well as translations, or the even more recent “spectre monotile“, which is similar except that no reflections are needed.)

One of the motivations of this conjecture was the observation of Hao Wang that if the periodic tiling conjecture were true, then the translational monotiling problem is (algorithmically) decidable: there is a Turing machine which, when given a dimension {d} and a finite subset {F} of {{\bf Z}^d}, can determine in finite time whether {F} can tile {{\bf Z}^d}. This is because if a periodic tiling exists, it can be found by computer search; and if no tiling exists at all, then (by the compactness theorem) there exists some finite subset of {{\bf Z}^d} that cannot be covered by disjoint translates of {F}, and this can also be discovered by computer search. The periodic tiling conjecture asserts that these are the only two possible scenarios, thus giving the decidability.

On the other hand, Wang’s argument is not known to be reversible: the failure of the periodic tiling conjecture does not automatically imply the undecidability of the translational monotiling problem, as it does not rule out the existence of some other algorithm to determine tiling that does not rely on the existence of a periodic tiling. (For instance, even with the newly discovered hat and spectre tiles, it remains an open question whether the isometric monotiling problem for (say) polygons with rational coefficients in {{\bf R}^2} is decidable, with or without reflections.)

The main result of this paper settles this question (with one caveat):

Theorem 1 There does not exist any algorithm which, given a dimension {d}, a periodic subset {E} of {{\bf Z}^d}, and a finite subset {F} of {{\bf Z}^d}, determines in finite time whether there is a translational tiling {A \oplus F = E} of {E} by {F}.

The caveat is that we have to work with periodic subsets {E} of {{\bf Z}^d}, rather than all of {{\bf Z}^d}; we believe this is largely a technical restriction of our method, and it is likely that can be removed with additional effort and creativity. We also remark that when {d=2}, the periodic tiling conjecture was established by Bhattacharya, and so the problem is decidable in the {d=2} case. It remains open whether the tiling problem is decidable for any fixed value of {d>2} (note in the above result that the dimension {d} is not fixed, but is part of the input).

Because of a well known link between algorithmic undecidability and logical undecidability (also known as logical independence), the main theorem also implies the existence of an (in principle explicitly describable) dimension {d}, periodic subset {E} of {{\bf Z}^d}, and a finite subset {F} of {{\bf Z}^d}, such that the assertion that {F} tiles {E} by translation cannot be proven or disproven in ZFC set theory (assuming of course that this theory is consistent).

As a consequence of our method, we can also replace {{\bf Z}^d} here by “virtually two-dimensional” groups {{\bf Z}^2 \times G_0}, with {G_0} a finite abelian group (which now becomes part of the input, in place of the dimension {d}).

We now describe some of the main ideas of the proof. It is a common technique to show that a given problem is undecidable by demonstrating that some other problem that was already known to be undecidable can be “encoded” within the original problem, so that any algorithm for deciding the original problem would also decide the embedded problem. Accordingly, we will encode the Wang tiling problem as a monotiling problem in {{\bf Z}^d}:

Problem 2 (Wang tiling problem) Given a finite collection {{\mathcal W}} of Wang tiles (unit squares with each side assigned some color from a finite palette), is it possible to tile the plane with translates of these tiles along the standard lattice {{\bf Z}^2}, such that adjacent tiles have matching colors along their common edge?

It is a famous result of Berger that this problem is undecidable. The embedding of this problem into the higher-dimensional translational monotiling problem proceeds through some intermediate problems. Firstly, it is an easy matter to embed the Wang tiling problem into a similar problem which we call the domino problem:

Problem 3 (Domino problem) Given a finite collection {{\mathcal R}_1} (resp. {{\mathcal R}_2}) of horizontal (resp. vertical) dominoes – pairs of adjacent unit squares, each of which is decorated with an element of a finite set {{\mathcal W}} of “pips”, is it possible to assign a pip to each unit square in the standard lattice tiling of {{\bf Z}^2}, such that every horizontal (resp. vertical) pair of squares in this tiling is decorated using a domino from {{\mathcal R}_1} (resp. {{\mathcal R}_2})?

Indeed, one just has to interpet each Wang tile as a separate “pip”, and define the domino sets {{\mathcal R}_1}, {{\mathcal R}_2} to be the pairs of horizontally or vertically adjacent Wang tiles with matching colors along their edge.

Next, we embed the domino problem into a Sudoku problem:

Problem 4 (Sudoku problem) Given a column width {N}, a digit set {\Sigma}, a collection {{\mathcal S}} of functions {g: \{0,\dots,N-1\} \rightarrow \Sigma}, and an “initial condition” {{\mathcal C}} (which we will not detail here, as it is a little technical), is it possible to assign a digit {F(n,m)} to each cell {(n,m)} in the “Sudoku board” {\{0,1,\dots,N-1\} \times {\bf Z}} such that for any slope {j \in {\bf Z}} and intercept {i \in {\bf Z}}, the digits {n \mapsto F(n,jn+i)} along the line {\{(n,jn+i): 0 \leq n \leq N-1\}} lie in {{\mathcal S}} (and also that {F} obeys the initial condition {{\mathcal C}})?

The most novel part of the paper is the demonstration that the domino problem can indeed be embedded into the Sudoku problem. The embedding of the Sudoku problem into the monotiling problem follows from a modification of the methods in our previous papers, which had also introduced versions of the Sudoku problem, and created a “tiling language” which could be used to “program” various problems, including the Sudoku problem, as monotiling problems.

To encode the domino problem into the Sudoku problem, we need to take a domino function {{\mathcal T}: {\bf Z}^2 \rightarrow {\mathcal W}} (obeying the domino constraints associated to some domino sets {{\mathcal R}_1, {\mathcal R}_2}) and use it to build a Sudoku function {F: \{0,\dots,N-1\} \times {\bf Z} \rightarrow \Sigma} (obeying some Sudoku constraints relating to the domino sets); conversely, every Sudoku function obeying the rules of our Sudoku puzzle has to arise somehow from a domino function. The route to doing so was not immediately obvious, but after a helpful tip from Emmanuel Jeandel, we were able to adapt some ideas of Aanderaa and Lewis, in which certain hierarchical structures were used to encode one problem in another. Here, we interpret hierarchical structure {p}-adically (using two different primes due to the two-dimensionality of the domino problem). The Sudoku function {F} that will exemplify our embedding is then built from {{\mathcal T}} by the formula

\displaystyle  F(n,m) := ( f_{p_1}(m), f_{p_2}(m), {\mathcal T}(\nu_{p_1}(m), \nu_{p_2}(m)) ) \ \ \ \ \ (1) where {p_1,p_2} are two large distinct primes (for instance one can take {p_1=53}, {p_2=59} for concreteness), {\nu_p(m)} denotes the number of times {p} divides {m}, and {f_p(m) \in {\bf Z}/p{\bf Z} \backslash \{0\}} is the last non-zero digit in the base {p} expansion of {m}:

\displaystyle  f_p(m) := \frac{m}{p^{\nu_p(m)}} \hbox{ mod } p (with the conventions {\nu_p(0)=+\infty} and {f_p(0)=1}). In the case {p_1=3, p_2=5}, the first component of (1) looks like this:

and a typical instance of the final component {{\mathcal T}(\nu_{p_1}(m), \nu_{p_2}(m))} looks like this:

Amusingly, the decoration here is essentially following the rules of the children’s game “Fizz buzz“.

To demonstrate the embedding, we thus need to produce a specific Sudoku rule {{\mathcal S}} (as well as a more technical initial condition {{\mathcal C}}, which is basically required to exclude degenerate Sudoku solutions such as a constant solution) that can “capture” the target function (1), in the sense that the only solutions to this specific Sudoku puzzle are given by variants of {F} (e.g., {F} composed with various linear transformations). In our previous paper we were able to build a Sudoku puzzle that could similarly capture either of the first two components {f_{p_1}(m)}, {f_{p_2}(m)} of our target function (1) (up to linear transformations), by a procedure very akin to solving an actual Sudoku puzzle (combined with iterative use of a “Tetris” move in which we eliminate rows of the puzzle that we have fully solved, to focus on the remaining unsolved rows). Our previous paper treated the case when {p} was replaced with a power of {2}, as this was the only case that we know how to embed in a monotiling problem of the entirety of {{\bf Z}^d} (as opposed to a periodic subset {E} of {{\bf Z}^d}), but the analysis is in fact easier when {p} is a large odd prime, instead of a power of {2}. Once the first two components {f_{p_1}(m), f_{p_2}(m)} have been solved for, it is a relatively routine matter to design an additional constraint in the Sudoku rule that then constrains the third component to be of the desired form {{\mathcal T}(\nu_{p_1}(m), \nu_{p_2}(m))}, with {{\mathcal T}} obeying the domino constraints.

I have just uploaded to the arXiv my paper “Monotone non-decreasing sequences of the Euler totient function“. This paper concerns the quantity {M(x)}, defined as the length of the longest subsequence of the numbers from {1} to {x} for which the Euler totient function {\varphi} is non-decreasing. The first few values of {M} are

\displaystyle  1, 2, 3, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 11, 12, 12, \dots

(OEIS A365339). For instance, {M(6)=5} because the totient function is non-decreasing on the set {\{1,2,3,4,5\}} or {\{1,2,3,4,6\}}, but not on the set {\{1,2,3,4,5,6\}}.

Since {\varphi(p)=p-1} for any prime {p}, we have {M(x) \geq \pi(x)}, where {\pi(x)} is the prime counting function. Empirically, the primes come quite close to achieving the maximum length {M(x)}; indeed it was conjectured by Pollack, Pomerance, and Treviño, based on numerical evidence, that one had

\displaystyle  M(x) = \pi(x)+64 \ \ \ \ \ (1)

for all {x \geq 31957}; this conjecture is verified up to {x=10^7}. The previous best known upper bound was basically of the form

\displaystyle  M(x) \leq \exp( (C+o(1)) (\log\log\log x)^2 ) \frac{x}{\log x} \ \ \ \ \ (2)

as {x \rightarrow \infty} for an explicit constant {C = 0.81781\dots}, from combining results from the above paper with that of Ford or of Maier-Pomerance. In this paper we obtain the asymptotic

\displaystyle  M(x) = \left( 1 + O \left(\frac{(\log\log x)^5}{\log x}\right) \right) \frac{x}{\log x}

so in particular {M(x) = (1+o(1))\pi(x)}. This answers a question of Erdős, as well as a closely related question of Pollack, Pomerance, and Treviño.

The methods of proof turn out to be mostly elementary (the most advanced result from analytic number theory we need is the prime number theorem with classical error term). The basic idea is to isolate one key prime factor {p} of a given number {1 \leq n \leq x} which has a sizeable influence on the totient function {\varphi(n)}. For instance, for “typical” numbers {n}, one has a factorization

\displaystyle  n = d p_2 p_1

where {p_2} is a medium sized prime, {p_1} is a significantly larger prime, and {d} is a number with all prime factors less than {p_2}. This leads to an approximation

\displaystyle  \varphi(n) \approx \frac{\varphi(d)}{d} (1-\frac{1}{p_2}) n.

As a consequence, if we temporarily hold {d} fixed, and also localize {n} to a relatively short interval, then {\varphi} can only be non-decreasing in {n} if {p_2} is also non-decreasing at the same time. This turns out to significantly cut down on the possible length of a non-decreasing sequence in this regime, particularly if {p_2} is large; this can be formalized by partitioning the range of {p_2} into various subintervals and inspecting how this (and the monotonicity hypothesis on {\varphi}) constrains the values of {n} associated to each subinterval. When {p_2} is small, we instead use a factorization

\displaystyle  n = d p \ \ \ \ \ (3)

where {d} is very smooth (i.e., has no large prime factors), and {p} is a large prime. Now we have the approximation

\displaystyle  \varphi(n) \approx \frac{\varphi(d)}{d} n \ \ \ \ \ (4)

and we can conclude that {\frac{\varphi(d)}{d}} will have to basically be piecewise constant in order for {\varphi} to be non-decreasing. Pursuing this analysis more carefully (in particular controlling the size of various exceptional sets in which the above analysis breaks down), we end up achieving the main theorem so long as we can prove the preliminary inequality

\displaystyle  \sum_{\frac{\varphi(d)}{d}=q} \frac{1}{d} \leq 1 \ \ \ \ \ (5)

for all positive rational numbers {q}. This is in fact also a necessary condition; any failure of this inequality can be easily converted to a counterexample to the bound (2), by considering numbers of the form (3) with {\frac{\varphi(d)}{d}} equal to a fixed constant {q} (and omitting a few rare values of {n} where the approximation (4) is bad enough that {\varphi} is temporarily decreasing). Fortunately, there is a minor miracle, relating to the fact that the largest prime factor of denominator of {\frac{\varphi(d)}{d}} in lowest terms necessarily equals the largest prime factor of {d}, that allows one to evaluate the left-hand side of (5) almost exactly (this expression either vanishes, or is the product of {\frac{1}{p-1}} for some primes {p} ranging up to the largest prime factor of {q}) that allows one to easily establish (5). If one were to try to prove an analogue of our main result for the sum-of-divisors function {\sigma(n)}, one would need the analogue

\displaystyle  \sum_{\frac{\sigma(d)}{d}=q} \frac{1}{d} \leq 1 \ \ \ \ \ (6)

of (5), which looks within reach of current methods (and was even claimed without proof by Erdos), but does not have a full proof in the literature at present.

In the final section of the paper we discuss some near counterexamples to the strong conjecture (1) that indicate that it is likely going to be difficult to get close to proving this conjecture without assuming some rather strong hypotheses. Firstly, we show that failure of Legendre’s conjecture on the existence of a prime between any two consecutive squares can lead to a counterexample to (1). Secondly, we show that failure of the Dickson-Hardy-Littlewood conjecture can lead to a separate (and more dramatic) failure of (1), in which the primes are no longer the dominant sequence on which the totient function is non-decreasing, but rather the numbers which are a power of two times a prime become the dominant sequence. This suggests that any significant improvement to (2) would require assuming something comparable to the prime tuples conjecture, and perhaps also some unproven hypotheses on prime gaps.

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