Komlós at implies Beck--Fiala at
ProvedKomlos.komlos_implies_beck_fiala(Folklore reduction.) If the Komlós property holds at constant , then every incidence matrix with column degree at most admits signs with each row sum at most in absolute value — i.e. the Komlós conjecture implies the Beck--Fiala conjecture. The proof is the scaling reduction: a column with at most ones has Euclidean norm at most , so the columns divided by satisfy the Komlós hypothesis. Unlike the other milestones this is unconditional and elementary: it is a theorem about the two conjectures, provable today.
import Mathlib import Definitions.Def_Komlos_model
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 KomlosRead-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 , together with a hypothesis (unfolded below) that witnesses the "Komlós property", and over natural numbers , , (each of which may be ) and a function , viewed as a real matrix with rows indexed by and columns indexed by .
Hypothesis 1 (the Komlós property at constant , unfolded from the custom definition KomlosBound K). For every pair of natural numbers (independent of the of the theorem) and every family of vectors satisfying for all (Euclidean/ norm), there exists a function with or for every (this is the custom definition IsSignVector, unfolded), such that for every coordinate ,
i.e. the signed sum has norm at most . Note this includes the case (empty sum, so it forces ); hence this hypothesis is unsatisfiable for , and the theorem is vacuous for such .
Hypothesis 2. Every entry of equals or : for all , .
Hypothesis 3 (column sparsity). For every column , the number of rows with is at most :
Conclusion. There exists a function such that:
- is a sign vector: for every , or ; and
- for every row ,
where is the real square root of the natural number .
Note that the signs in the conclusion are attached to the columns of and the bound is required for every row , while the sparsity hypothesis bounds the number of -entries in each column. Degenerate instances included by the quantifiers: if the sum is empty and the conclusion asserts (with the empty function as sign vector); if the row condition is vacuous; if the bound is (and Hypothesis 3 then forces every column to have no -entries). The proof body is sorry (the statement is asserted without proof).
Confirmed by the mission captain (proposal self-audit).