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Komlós at KKK implies Beck--Fiala at KtK\sqrt{t}Kt​

Proved
Komlos.komlos_implies_beck_fiala

by Shuze Chen · Sep 3, 2026 · Mathlib 0df444a (Lean v4.33.1)

beck-fialadiscrepancyreduction

(Folklore reduction.) If the Komlós property holds at constant KKK, then every 0/10/10/1 incidence matrix with column degree at most ttt admits signs with each row sum at most KtK\sqrt{t}Kt​ in absolute value — i.e. the Komlós conjecture implies the Beck--Fiala conjecture. The proof is the scaling reduction: a column with at most ttt ones has Euclidean norm at most t\sqrt{t}t​, so the columns divided by t\sqrt{t}t​ satisfy the Komlós hypothesis. Unlike the other milestones this is unconditional and elementary: it is a theorem about the two conjectures, provable today.

Preamble
import Mathlib
import Definitions.Def_Komlos_model
Formal statement
namespace Komlos

theorem komlos_implies_beck_fiala (K : ℝ) (hK : KomlosBound K)
    (t n m : ℕ) (A : Fin m → Fin n → ℝ)
    (h01 : ∀ i j, A i j = 0 ∨ A i j = 1)
    (hdeg : ∀ j, ({i | A i j = 1} : Finset (Fin m)).card ≤ t) :
    ∃ ε : Fin n → ℝ, IsSignVector ε ∧
      ∀ i, |∑ j, A i j * ε j| ≤ K * Real.sqrt t := by sorry

end Komlos
Source
Folklore; stated e.g. in Bansal--Dadush--Garg, An algorithm for Komlos conjecture matching Banaszczyk's bound, Section 1, https://arxiv.org/abs/1605.02882
Read-back

What the Lean code literally says, in plain math · claude-fable-5

Read-back: Komlos.komlos_implies_beck_fiala

The theorem is universally quantified over a real number KKK, together with a hypothesis (unfolded below) that KKK witnesses the "Komlós property", and over natural numbers ttt, nnn, mmm (each of which may be 000) and a function A:{0,…,m−1}×{0,…,n−1}→RA : \{0,\dots,m-1\} \times \{0,\dots,n-1\} \to \mathbb{R}A:{0,…,m−1}×{0,…,n−1}→R, viewed as a real m×nm \times nm×n matrix with rows indexed by i∈{0,…,m−1}i \in \{0,\dots,m-1\}i∈{0,…,m−1} and columns indexed by j∈{0,…,n−1}j \in \{0,\dots,n-1\}j∈{0,…,n−1}.

Hypothesis 1 (the Komlós property at constant KKK, unfolded from the custom definition KomlosBound K). For every pair of natural numbers n′,m′n', m'n′,m′ (independent of the n,m,tn, m, tn,m,t of the theorem) and every family of vectors v0,…,vn′−1∈Rm′v_0, \dots, v_{n'-1} \in \mathbb{R}^{m'}v0​,…,vn′−1​∈Rm′ satisfying ∥vi∥≤1\lVert v_i \rVert \le 1∥vi​∥≤1 for all iii (Euclidean/ℓ2\ell^2ℓ2 norm), there exists a function ε:{0,…,n′−1}→R\varepsilon : \{0,\dots,n'-1\} \to \mathbb{R}ε:{0,…,n′−1}→R with εi=1\varepsilon_i = 1εi​=1 or εi=−1\varepsilon_i = -1εi​=−1 for every iii (this is the custom definition IsSignVector, unfolded), such that for every coordinate j∈{0,…,m′−1}j \in \{0,\dots,m'-1\}j∈{0,…,m′−1},

∣∑i=0n′−1εi vi(j)∣≤K,\Bigl|\sum_{i=0}^{n'-1} \varepsilon_i \, v_i(j)\Bigr| \le K,​i=0∑n′−1​εi​vi​(j)​≤K,

i.e. the signed sum has ℓ∞\ell^\inftyℓ∞ norm at most KKK. Note this includes the case n′=0n' = 0n′=0 (empty sum, so it forces 0≤K0 \le K0≤K); hence this hypothesis is unsatisfiable for K<0K < 0K<0, and the theorem is vacuous for such KKK.

Hypothesis 2. Every entry of AAA equals 000 or 111: for all i,ji, ji,j, Aij=0∨Aij=1A_{ij} = 0 \lor A_{ij} = 1Aij​=0∨Aij​=1.

Hypothesis 3 (column sparsity). For every column j∈{0,…,n−1}j \in \{0,\dots,n-1\}j∈{0,…,n−1}, the number of rows iii with Aij=1A_{ij} = 1Aij​=1 is at most ttt:

#{ i∈{0,…,m−1}:Aij=1 }≤t.\#\{\, i \in \{0,\dots,m-1\} : A_{ij} = 1 \,\} \le t.#{i∈{0,…,m−1}:Aij​=1}≤t.

Conclusion. There exists a function ε:{0,…,n−1}→R\varepsilon : \{0,\dots,n-1\} \to \mathbb{R}ε:{0,…,n−1}→R such that:

  • ε\varepsilonε is a sign vector: for every jjj, εj=1\varepsilon_j = 1εj​=1 or εj=−1\varepsilon_j = -1εj​=−1; and
  • for every row i∈{0,…,m−1}i \in \{0,\dots,m-1\}i∈{0,…,m−1},
∣∑j=0n−1Aij εj∣≤Kt,\Bigl|\sum_{j=0}^{n-1} A_{ij}\, \varepsilon_j\Bigr| \le K \sqrt{t},​j=0∑n−1​Aij​εj​​≤Kt​,

where t\sqrt{t}t​ is the real square root of the natural number ttt.

Note that the signs in the conclusion are attached to the columns of AAA and the bound is required for every row iii, while the sparsity hypothesis bounds the number of 111-entries in each column. Degenerate instances included by the quantifiers: if n=0n = 0n=0 the sum is empty and the conclusion asserts 0≤Kt0 \le K\sqrt{t}0≤Kt​ (with the empty function as sign vector); if m=0m = 0m=0 the row condition is vacuous; if t=0t = 0t=0 the bound is K⋅0=0K \cdot 0 = 0K⋅0=0 (and Hypothesis 3 then forces every column to have no 111-entries). The proof body is sorry (the statement is asserted without proof).

Human review
  • Endorsed by Community (Bot) · Sep 3, 2026

  • Endorsed by Shuze Chen · Sep 3, 2026

    Confirmed by the mission captain (proposal self-audit).

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