Bound · version 1

The dimension-free unit-slack inequality

Statement

For every n ≥ 1 and independent nonnegative integrable X₁,…,Xₙ with E Xᵢ ≤ 1, P(S<ES+1)≥e−1\mathbb P(S<\mathbb ES+1)\ge e^{-1}, where S=∑iXiS=\sum_i X_i.

Assumptions

  • Positive finite number of independent variables.
  • Nonnegative variables with each mean at most one; strict threshold.

Scope

The conventional unit-slack conjecture. The stronger finite-n theorem yields this bound and approaches 1/e as n grows.

Explanation

Bounds & computations · 1

Compare the quantity and hypotheses within each group. A finite check, conditional theorem and proposed improvement cover different scopes.

Unit-slack probability
ResultValueConditions
The dimension-free unit-slack inequality →

Proved

Selected source date:

≥ 1/eAll positive finite dimensions; independent nonnegative variables with means at most one, strict threshold.

Evidence

Source reports

Connections

In this problem · publication v1 · Selected context

Feige’s probability conjecture →

Primary formulation · Proved

Related statements

The finite constants (n/(n+1))^n decrease to 1/e; each is strictly larger than 1/e.

Finite-n sharpness and the limiting universal bound are not identical statements.

Details & sources
The optimal bound for a fixed number of variables →

Proved · version 1

For every n ≥ 1, the optimal universal lower bound for P(S<ES+1)\mathbb P(S<\mathbb ES+1) is (nn+1)n\left(\frac n{n+1}\right)^n, over independent nonnegative integrable X₁,…,Xₙ with E Xᵢ ≤ 1.

  • Fix positive finite n.
  • Strict unit-slack event and individual means at most one.

Scope: Both July arguments support the same mathematical assertion. The pinned Lean declaration formalizes this fixed-dimensional optimality statement.

The dimension-free unit-slack inequality →

Proved · version 1

For every n ≥ 1 and independent nonnegative integrable X₁,…,Xₙ with E Xᵢ ≤ 1, P(S<ES+1)≥e−1\mathbb P(S<\mathbb ES+1)\ge e^{-1}, where S=∑iXiS=\sum_i X_i.

  • Positive finite number of independent variables.
  • Nonnegative variables with each mean at most one; strict threshold.

Scope: The conventional unit-slack conjecture. The stronger finite-n theorem yields this bound and approaches 1/e as n grows.

Relationship not independently reviewed.

Connection in Feige’s probability conjecture →

Relationship not independently reviewed. This concerns its use in this problem.

In this problem · publication v7

Feige’s probability conjecture →

Primary formulation · Proved

Related statements

The finite constants (n/(n+1))^n decrease to 1/e; each is strictly larger than 1/e.

Finite-n sharpness and the limiting universal bound are not identical statements.

Details & sources
The optimal bound for a fixed number of variables →

Proved · version 1

For every n ≥ 1, the optimal universal lower bound for P(S<ES+1)\mathbb P(S<\mathbb ES+1) is (nn+1)n\left(\frac n{n+1}\right)^n, over independent nonnegative integrable X₁,…,Xₙ with E Xᵢ ≤ 1.

  • Fix positive finite n.
  • Strict unit-slack event and individual means at most one.

Scope: Both July arguments support the same mathematical assertion. The pinned Lean declaration formalizes this fixed-dimensional optimality statement.

The dimension-free unit-slack inequality →

Proved · version 1

For every n ≥ 1 and independent nonnegative integrable X₁,…,Xₙ with E Xᵢ ≤ 1, P(S<ES+1)≥e−1\mathbb P(S<\mathbb ES+1)\ge e^{-1}, where S=∑iXiS=\sum_i X_i.

  • Positive finite number of independent variables.
  • Nonnegative variables with each mean at most one; strict threshold.

Scope: The conventional unit-slack conjecture. The stronger finite-n theorem yields this bound and approaches 1/e as n grows.

Relationship not independently reviewed.

Connection in Feige’s probability conjecture →

Relationship not independently reviewed. This concerns its use in this problem.

Sources

On Feige’s conjecture →

Zipei Nie · Jiaye Wei · Preprint · Publication date Jul 27, 2026

Revision & attribution

Source revision: 2026-07-27. Observed Oct 13, 2026.

Nie and Wei credit GPT-5.6 Sol for exploration and discovery, state that they checked the arguments and authored the text, and retain responsibility. Their argument uses the Vlassis–Thomas calibration theorem.

Sharp small-deviation inequalities for sums of independent nonnegative random variables →

Weibo Fu · Yanjun Han · Guanyang Wang · Jun Yan · Peng Zhang · Zhengqing Zhou · Preprint · Publication date Jul 27, 2026

Revision & attribution

Source revision: 2026-07-27. Observed Oct 13, 2026.

The authors report GPT-5.6 Pro assistance in proof discovery; the calibration theorem of Vlassis and Thomas and Grünbaum-type inequalities are mathematical inputs, not authors of this paper.

Lean proof of the sharp unit-slack Feige bound →

Guanyang Wang · Peng Zhang · Project documentation

Public formal source; execution and statement correspondence are separate checks.

Revision & attribution

Source revision: 19c61d6cb556d1e967dbb0ada9f293c290837867. Observed Oct 13, 2026.

The project credits GPT-5.6 Sol Codex for formalization. This is a different contribution from the July discovery model. Authors report verification; OpenMath has not executed it.

History

Statement versions

Statement versions and problem publication versions are separate. A new publication can update context or sources while retaining the same statement.

Assessment history · 2

Assessments retained in these publication records. Earlier statement versions retain their own source histories.

Record details
Claim ID
claim-feige-unit-slack
Statement version
1
SHA-256
b955f33690953767f18856c5922298322b37125b939ac1a55a8c015ff26fb18e

Publication records