Angular discrepancy bounds in terms of polynomial sparsity

Can the degree in an Erdős–Turán angular-discrepancy bound for polynomial roots be replaced by the number ν of nonzero terms, giving a bound of order √(ν log M) in terms of Mahler measure M?

Why it matters and what counts as progress

Why it matters: A sparse bound of this form would control root angles through description size rather than degree.

What counts as progress: A universal square-root bound or sparse polynomials with controlled Mahler measure and angular discrepancy exceeding it, with multiplicities counted consistently.

How the problem developed

  1. 1950 · Theorem [1]

    Paul Erdős and Pál Turán Proved a degree- and coefficient-sensitive bound for angular discrepancy of polynomial roots, establishing the classical baseline for the problem.

  2. 1972 · Partial result [2]

    W. K. Hayman Developed angular value-distribution results for power series with gaps, a related line of work on sparse supports.

  3. 8 Apr 2026 · Counterexample [3][4]

    Alexeev and coauthors, with an internal OpenAI model Gave an explicit polynomial family refuting a proposed fewnomial angular-discrepancy bound; the degree-based Erdős–Turán estimate remains distinct.

AI contribution

Short proofs in combinatorics, probability and number theory II

Boris Alexeev, Moe Putterman, Mehtaab Sawhney, Mark Sellke, Gregory Valiant

There is no universal Erdős–Turán angular-discrepancy bound of order √(ν(f) log M(f)) with polynomial degree replaced by the number ν(f) of nonzero terms. For each N, a polynomial with ν=N+2 and M<3 has a positive real zero of multiplicity N+1, giving discrepancy at least N+1/2 on the stated short arc.

AI involvementAI-led
ImportanceNotable
Technical verificationFormal (human)
Human understandingFull
Paper date:

Sources

  1. On the distribution of roots of polynomials
  2. Angular Value Distribution of Power Series with Gaps
  3. Alexeev and coauthors, Erdős Problem 990 proof
  4. Erdős Problem 990: angular discrepancy and polynomial sparsity
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