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Research Article · Preprint Mathematics

Relative finite optimization of arithmetic approximation distances

Scott Schubr

Luminary Oddities

  • Mathematics
  • Number Theory
  • Numerical Analysis
Primary subject
Mathematics
Subject subfields
Number Theory · Numerical Analysis
Paper type
Research Article
Manuscript stage
Preprint
Manuscript
Archived
Edition
2 · Permanent link

Overview

Abstract

We give finite rational approximations to the complete least-squares distance from the constant sequence to fractional-part sequences {n/k} in the norm Σ(n≥1) |f(n)|²/[n(n+1)]. The exact periodic mean is a positive gcd covariance form. Montgomery–Vaughan's circular Hilbert inequality yields a coefficient-uniform complete-tail error with constant D=N(N−1). This gives rational lower and upper optimization problems and a midpoint optimizer whose actual squared error is at most (1−β²)⁻¹ times the complete optimum, where β=2D(2D+1)/[(R+1)(R+2)]<1. The latter is a Kantorovich application and gives a sufficient cutoff R=O(N²ε^(−1/4)) for relative 1+ε quality of the optimizer. The computed optimum-value brackets are separate and retain their explicitly stated widths. An adjacent arithmetic wavelet shows that no bounded uniform prefix extension is possible at R∼N log N; at R∼N^(1+α) its full-to-prefix ratio tends to 1/α. The mean, Hilbert inequality, wavelet head identity and quadratic optimization tools are classical or previously established; their combination gives explicit native-norm truncation guarantees. A standard-library exact rational implementation is included.

Keywords Nyman–Beurling criterion · Báez-Duarte distance · arithmetic approximation · complete tail · Kantorovich inequality · exact rational optimization · fractional parts

Scope of the work

In the complete fractional-part sequence norm, rational finite optimization gives a candidate-quality factor (1−β²)⁻¹ for β<1 and sufficient cutoff O(N²ε^(−1/4)); exact adjacent-wavelet laws obstruct uniformly bounded short-prefix extension.

Reproduction notes

The 8-page manuscript and complete main.tex accompany build.py, CITATION.cff, README.md, MANIFEST.sha256, verify.py and results.json in an 8-member package. README.md gives Tectonic and Python 3.9+ standard-library commands. The verifier reproduces 12,333 exact rational controls and native optimization examples at N=4,8,12, retaining separate optimum brackets, candidate-norm brackets and quality factors. Universal infinite-tail bounds are proved analytically from the attributed Montgomery–Vaughan inequality.

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Author disclosures

AI assistants supported derivation, literature comparison, exposition and independent checks. The author is responsible for the mathematical statements and presentation.

A stable scholarly record

Cite this edition

Scott Schubr. (2026). Relative finite optimization of arithmetic approximation distances (Version 2). Luminary Research Archive. https://research.luminaryoddities.com/relative-finite-optimization-arithmetic-approximation-distances/v/2

BibTeX
@techreport{luminary_relative_finite_optimization_arithmetic_approximation_distances_v2,
  title = {Relative finite optimization of arithmetic approximation distances},
  author = {Scott Schubr},
  year = {2026},
  institution = {Luminary Research Archive},
  type = {Research Article},
  note = {Version 2},
  url = {https://research.luminaryoddities.com/relative-finite-optimization-arithmetic-approximation-distances/v/2}
}

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