On a problem of Erdős and Ingham (2512.16528v1)
Abstract: We give a short and elementary argument answering a question of Erdős and Ingham negatively. Erdős and Ingham showed that a Tauberian estimate they considered was equivalent to the non-vanishing of $1+\sum_{k}a_k{-1-it}$ for any real number $t$ and any sequence $1<a_1<a_2<\cdots$ of positive integers such that $\sum_k a_k{-1}<\infty$. We disprove this statement. In fact, we show that for any complex number $λ$ and any non-zero real number $t$, there exists a sequence $1<a_1<a_2<\cdots$ of positive integers such that $\sum_k a_k{-1}<\infty$ and $\sum_k a_k{-1-it} = λ$.
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Overview
This paper looks at a special kind of infinite sum that uses complex numbers. Two famous mathematicians, Erdős and Ingham, wondered whether a certain sum could ever equal zero under specific conditions. This paper shows, with a simple argument, that the answer to their question is “no”—meaning the sum can be zero—and even more: you can make the sum equal any complex number you want.
Main Topic and Purpose
The paper studies sums that look like this:
- Pick a sequence of positive integers
a1 < a2 < a3 < ...with the property that the sum of their reciprocals1/a1 + 1/a2 + ...is finite (so the numbers grow fast enough). - Consider the complex sum
1 + Σ (1 / a_k^(1 + it)), wheretis a real number andiis the imaginary unit.
Erdős and Ingham asked: is this sum always nonzero for every real t? This paper shows the answer is no for every nonzero t. In fact, the author proves something stronger: for any complex number λ and any nonzero t, you can choose the sequence so that Σ (1 / a_k^(1 + it)) = λ.
Key Questions in Simple Terms
- If we carefully pick a sequence of integers whose reciprocals add up to a finite number, is the complex sum
1 + Σ (1 / a_k^(1 + it))ever zero? - More strongly: can we make
Σ (1 / a_k^(1 + it))equal any target complex number we want?
The paper answers “yes” to the second question (you can hit any target), which automatically answers “no” to the first (the sum can be zero when the target is λ = -1).
Methods and Approach (Explained Simply)
The method is constructive: the author shows how to build the sequence step by step so that the complex sum lands exactly at the desired target λ.
Think of complex numbers as arrows on a plane:
- Each term
1 / n^(1 + it)is like a small arrow. - Its length is about
1/n. - Its direction depends on
tandn(roughly, it rotates by an angle related tot·log n).
Key idea:
- Over a short stretch of consecutive integers (say from
xto about(1 + ε)x), the arrows are nearly parallel and of similar size. Adding them gives one arrow pointing in a predictable direction, with a size roughly proportional to how many terms you included. - By carefully choosing such short stretches (blocks), you can add arrows that point almost exactly toward your target
λand have about the right length. This lets you “steer” the sum towardλ.
The proof has two parts:
- A lemma (a helpful fact):
- For any small complex “target arrow”
c, you can find a short block of integers (all large, starting beyond any chosenN) whose sum of1 / n^(1 + it)is very close toc. - The “cost” of using that block—measured by
Σ (1/n)—is small (at most|c|). - The error is tiny (on the order of
|c|^2).
- For any small complex “target arrow”
Everyday analogy: You have a joystick that nudges a point in the plane by a small arrow. The lemma says you can always produce a small nudge in almost any direction, with very little energy cost.
- The main construction:
- Start with your target
λ. - Repeatedly use the lemma to add a new block that reduces the remaining gap to the target. If the gap is big, add a medium-size nudge; if it’s small, add a smaller nudge. Each step cuts the gap down significantly (at least by half once it’s small).
- Because the errors get quadratically smaller and the “costs” add up to a finite total, the process converges: the sum of all those small arrows equals exactly
λ, and the totalΣ (1/n)stays finite.
- Start with your target
This is like walking toward a point by taking carefully aimed steps that get shorter and more precise as you approach, making sure you never overspend your energy budget.
Main Findings and Why They Matter
- Main theorem: For any nonzero real
tand any complex numberλ, there exists an infinite set of integersSwithΣ_{n∈S} (1/n) < ∞such thatΣ_{n∈S} (1 / n^(1 + it)) = λ. - Immediate consequence: Take
λ = -1. Then1 + Σ (1 / n^(1 + it)) = 0. So the answer to Erdős and Ingham’s original question is negative: the sum can be zero.
Why this matters:
- Erdős and Ingham showed their “nonzero” condition was equivalent to a powerful statement in analysis (a type of Tauberian estimate: roughly, a rule that tells you how a function behaves at infinity based on certain average-like sums). Since the nonzero condition fails, that equivalence can’t be used to guarantee the Tauberian estimate in general.
- Conceptually, it shows these complex sums are very flexible: with a thin enough sequence (so that
Σ 1/nconverges), you can “steer” the sum to any complex target.
Implications and Impact
- The paper settles a long-standing question negatively and provides a simple method to construct counterexamples.
- It highlights an important boundary: while infinite sequences can be manipulated to hit any target, the situation may be very different for finite sequences.
Open direction raised by the paper:
- The author conjectures that if the sequence is finite, then
1 + Σ (1 / n^(1 + it))is never zero. - Even the small, specific case
S = {2, 3, 5}is still unknown: is1 + 2^(-1 - it) + 3^(-1 - it) + 5^(-1 - it)ever zero for somet?
In short:
- Infinite “thin” sequences give you total control over the complex sum.
- Finite sequences might be much stricter, and figuring out whether they can make the sum zero is an open and intriguing problem.
Knowledge Gaps
Knowledge gaps, limitations, and open questions
Below is a concise list of unresolved issues and limitations left by the paper, each framed to suggest concrete directions for future work:
- Finite-set case remains open: Prove or disprove the conjecture that for any finite and any real , , with special attention to the explicit test case .
- Near-zeros for finite sets: Determine whether, for a fixed finite , the function can be made arbitrarily small (in modulus) for some real . If so, quantify how small and under what diophantine conditions on .
- Tauberian consequences: Since Erdős–Ingham’s Tauberian estimate is equivalent to non-vanishing, provide an explicit counterexample function (non-decreasing, vanishing on ) witnessing the failure of the Tauberian implication when the constructed infinite makes .
- Explicit quantitative bounds: Strengthen the existence proof by giving explicit rates and finite-stage guarantees—e.g., for a given , bound the number of terms needed to achieve and the corresponding upper bound on as functions of , , and .
- Tight trade-offs for the reciprocal sum: The proof asserts “for any ,” but is fixed in the main construction. Make this precise by showing how to tune (and all dependent bounds) to achieve an arbitrary target , and determine lower bounds on necessary to realize a given .
- Selection of scales : The lemma requires to control errors. The main construction sets only “greater than previous elements.” Clarify and formalize the choice of to ensure the lemma’s hypotheses hold at each step, and analyze the impact on the growth/location of .
- Structural constraints on : Investigate whether can be achieved under additional arithmetic structure, such as when is restricted to primes, squarefree numbers, a fixed residue class, or sets with prescribed sparsity (e.g., lacunary sets or given asymptotic density).
- Uniformity in : The constants and error terms depend on . Establish uniform versions (e.g., for in a compact set not containing $0$) and analyze the behavior as .
- Generalization beyond integers: Explore whether the result extends (or simplifies) when are allowed to be arbitrary reals , or when weights are introduced (e.g., complex coefficients with ), and characterize the set of realizable under such constraints.
- Geometric characterization for finite sets: For fixed finite , describe the image in the complex plane (e.g., its closure, convexity, and whether it intersects ), and provide criteria on ensuring non-vanishing.
- Minimal-complexity constructions: Determine whether there exists an infinite with additional regularity (e.g., a single interval, bounded cluster sizes, or bounded gaps) that still realizes an arbitrary , and quantify the minimal number/size of clusters needed.
Glossary
- Absolute convergence: A property of an infinite series where the series formed by the absolute values of its terms also converges; it guarantees convergence regardless of term order. "and the series $\sum_{n\in S} \frac{1}{n^{1 + it}$ is absolutely convergent."
- Argument (complex): The angle of a complex number in the complex plane, typically measured from the positive real axis. "has modulus and argument ."
- Asymptotic equivalence (∼): The relation meaning . "f(x) + \sum_k f(x/a_k) \sim \left(1 + \sum_k a_k{-1}\right)x"
- Implied constant: An unspecified constant hidden in asymptotic or Big-O bounds that depends only on specified parameters. "where the implied constant (which may be taken to be $1 + |1 + it|$) depends only on ."
- Line Re = 1: The vertical line in the complex plane consisting of points whose real part equals 1. "zeta-like series on the line "
- Mean value theorem: A calculus result stating that for a differentiable function on an interval, there exists a point where the derivative equals the average rate of change over the interval. "By the mean value theorem, we have"
- Modulus (complex): The magnitude or absolute value of a complex number, equal to its distance from the origin in the complex plane. "has modulus "
- Non-vanishing: The property of a function or expression not taking the value zero on a specified set. "equivalent to the non-vanishing of "
- Parallel (complex numbers): Two complex numbers are parallel if they have the same argument (one is a positive real multiple of the other). "Take such that is parallel to ."
- Tauberian estimate: An analytic estimate from Tauberian theory connecting the behavior of a series or transform to the asymptotics of a summatory function, often providing converses to Abelian theorems. "Erd\H{o}s and Ingham showed that a Tauberian estimate they considered was equivalent"
- Vanishes on [0, 1): A function is identically zero throughout the interval . "For any non-decreasing which vanishes on ,"
- Zeta-like series: A Dirichlet-type series resembling the Riemann zeta function, typically of the form for complex . "zeta-like series on the line "
Practical Applications
Overview
The paper gives a constructive, elementary method to build infinite integer sets S with convergent sum of reciprocals such that the Dirichlet series ∑_{n∈S} n{-(1+it)} attains any prescribed complex value λ for any fixed t ≠ 0. This provides a negative answer to an Erdős–Ingham question about non-vanishing on the line Re(s)=1, and yields a practical “value-matching” procedure based on phase-aligned block summation over short intervals.
Below are practical applications of the result and its constructive method, grouped by immediacy, with sectors, potential tools/workflows, and key dependencies/assumptions.
Immediate Applications
- Counterexample and stress-test generator for Dirichlet series and Tauberian claims
- Sectors: academia (analytic number theory, harmonic analysis), software (CAS/numerical libraries)
- What: Use the construction with λ = −1 to build explicit sequences S where 1 + ∑_{n∈S} n{-(1+it)} = 0, contradicting blanket non-vanishing assumptions. Generate families of adversarial sequences to test robustness of analytic arguments or library functions that implicitly assume non-vanishing on Re(s)=1 for “sparse” supports.
- Tools/workflows:
- “Dirichlet-Target-Synthesizer”: a routine taking (t, λ, N_min, ε) and returning a finite truncation of S that approximates the target value within ε while keeping ∑ 1/n bounded.
- Regression tests for complex-power evaluation and summation routines near the boundary of absolute convergence.
- Dependencies/assumptions:
- Requires t ≠ 0 and infinite S in theory; in practice, use finite truncations with error control.
- Accurate complex exponentiation and summation with controlled rounding error.
- Verified-target benchmarks for numerical summation and convergence acceleration
- Sectors: software (scientific computing, numerical analysis)
- What: Curate test instances with known ground-truth sums (λ). Use them to evaluate series acceleration, Kahan/compensated summation, and oscillatory sum methods near critical lines where cancellation is severe.
- Tools/workflows:
- Benchmark suites with parameter sweeps in t, |λ|, and truncation depth.
- Comparative reports for summation algorithms on oscillatory, slowly varying kernels.
- Dependencies/assumptions:
- Truncation error estimates derived from the paper’s O(|c|2) stepwise error and geometric decay of residuals.
- Numerical stability for large x and small phase increments.
- Pedagogical modules on constructive counterexamples and boundary behavior of Dirichlet series
- Sectors: education (advanced undergraduate/graduate analysis, number theory)
- What: Classroom demonstrations of (i) constructing counterexamples, (ii) using slowly varying phases to steer complex sums, and (iii) limitations of Tauberian implications without explicit non-vanishing hypotheses.
- Tools/workflows:
- Interactive notebooks allowing students to choose t, λ and watch the iterative assembly of S and the convergence of partial sums.
- Dependencies/assumptions:
- Clear exposition of the phase-aligned interval idea and its error bound.
- Visualizations of complex partial sums.
- Phase-aligned block summation heuristic for oscillatory sums/integrals
- Sectors: numerical analysis, computational physics/engineering
- What: The “short-interval, nearly-constant phase” idea provides a reusable heuristic for computing oscillatory sums ∑ f(n) e{-it log n}/n by grouping terms into short windows where the phase is nearly constant, improving cancellation control.
- Tools/workflows:
- Windowed summation schemes that align window centers so that local phasors add coherently, then refine by decreasing window size as the residual shrinks.
- Dependencies/assumptions:
- The integrand exhibits slow phase variation over small relative intervals; requires tunable window length linked to target accuracy.
- Care in regions where f(n) varies faster than the phase approximation.
- Audits of applied results relying on non-vanishing of Dirichlet-type transforms
- Sectors: applied mathematics, data science (when Abelian/Tauberian heuristics are used)
- What: Review analyses or algorithms that may have implicitly assumed that 1 + ∑ a_k{-(1+it)} ≠ 0 for all t. The paper shows such assumptions can fail for admissible sequences; downstream inferences or stability claims should include explicit checks.
- Tools/workflows:
- Checklists and automated diagnostics to detect when a method’s correctness depends on non-vanishing for arbitrary supports.
- Dependencies/assumptions:
- The specific class of sequences used in the application must match the paper’s setting (arbitrary infinite subset with ∑ 1/a_k < ∞). If supports are structured (e.g., primes only or finite sets), conclusions may differ.
Long-Term Applications
- Multi-frequency design: matching targets at several frequencies simultaneously
- Sectors: academia (harmonic analysis), software (stress-testing spectral algorithms)
- What: Extend the constructive method to choose S so that ∑_{n∈S} n{-(1+i t_j)} ≈ λ_j for multiple t_j. This would create powerful adversarial or calibration datasets for multi-frequency Tauberian-type analyses and spectral estimation tools.
- Potential tools/products:
- Multi-objective sequence designer with trade-offs between total cost ∑ 1/n and vector error norm in the target space.
- Dependencies/assumptions:
- Nontrivial interaction between phase-alignment at different t_j; may require more intricate block scheduling and could inflate ∑ 1/n.
- Further theoretical development to guarantee simultaneous approximation rates.
- Finite-set regime: resolving the open finite-support non-vanishing question
- Sectors: academia (number theory, combinatorics), software (finite-instance generators)
- What: If the conjectured non-vanishing for finite S is proved, it gives sharp boundaries between what can and cannot be realized with finite vs. infinite supports—informing discrete design constraints. If disproved, constructive finite counterexamples would provide compact, high-impact test cases.
- Potential tools/products:
- Exhaustive/heuristic search frameworks to explore small finite sets S for specific t and detect near-zeros/zeros with certified bounds.
- Dependencies/assumptions:
- Significant new number-theoretic insights or algorithmic advances; careful certification of zeros given floating-point limitations.
- Generalized kernels and structured supports
- Sectors: academia (analytic number theory, harmonic analysis), numerical analysis
- What: Adapt the phase-aligned block construction to other kernels (e.g., n{-s} with s near 1, or weights restricted to structured sets such as primes or polynomial sequences). This could inform the design of synthetic Dirichlet series with prescribed boundary behavior for algorithm evaluation.
- Potential tools/products:
- Libraries of synthetic series instances with tunable regularity and support structure for benchmarking inference methods that rely on Abelian/Tauberian correspondences.
- Dependencies/assumptions:
- The “slowly varying phase” property must hold in the chosen setting; prime-restricted supports may require new techniques or conjectural inputs.
- Robust Tauberian pipelines with explicit non-vanishing checks
- Sectors: applied mathematics, econometrics, queueing theory, signal processing
- What: Incorporate explicit non-vanishing conditions (or certified numerical checks) into Tauberian-style inference pipelines to avoid false conclusions when boundary values can be engineered to vanish or nearly vanish.
- Potential tools/products:
- Preconditioners and validators that numerically assess proximity to zeros on Re(s)=1 and adapt inference thresholds accordingly.
- Dependencies/assumptions:
- Availability of reliable numerical probes near the boundary where condition numbers can be large; domain-specific adaptation of thresholds and error models.
- Educational and outreach ecosystems for constructive counterexamples
- Sectors: education, scientific communication
- What: Longer-term curricular development emphasizing constructive methods that turn “existence” into “algorithms,” fostering intuition about oscillatory phenomena and boundary behaviors.
- Potential tools/products:
- Open educational resources with interactive visualizations, guided projects, and automated graders leveraging the paper’s algorithmic ideas.
- Dependencies/assumptions:
- Sustained adoption in curricula and integration with computational platforms (e.g., SageMath, Jupyter).
Notes on core assumptions and dependencies shared across applications:
- The method crucially uses t ≠ 0 and infinite sequences; finite-set behavior is currently conjectural and may diverge qualitatively.
- Constructive steps rely on choosing real x so that x{-it} matches a target phase; in implementations, x = exp(−(arg c + 2πm)/t) for suitable integers m, followed by integer windows [x, x + s). Numerically, careful handling of phase wrapping and floating-point error is required.
- Error control is geometric due to the O(|c|2) step error and residual halving; practical implementations should expose tolerance controls and report the growth of the largest n used.
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