Ehrhard reduction of a failed convex-fold inequality to a planar counterexample
OpenKomlos.closedConvexFold_counterexample_planarbanaszczykconvex-geometrygaussian-measure
Let be compact and convex, with standard Gaussian measure , and let . Let be its explicit closed convex fold. Write , , and . For real parameters define
If , then there are satisfying
This isolates the geometric reduction from a general convex body to the planar strip comparison. It includes the Gaussian symmetrization and the comparison-line construction; the scalar Gaussian integral inequality is a separate theorem.
Preamble
import Definitions.Def_Komlos_gaussian_fold_analysis import Definitions.Def_Komlos_convex_fold import Mathlib.Probability.Distributions.Gaussian.Multivariate open Set MeasureTheory ProbabilityTheory GaussianFoldAnalysis open scoped intervalIntegral set_option autoImplicit false
Formal statement
theorem Komlos.closedConvexFold_counterexample_planar
(m : ℕ) (K : Set (EuclideanSpace ℝ (Fin m)))
(hconv : Convex ℝ K) (hcomp : IsCompact K)
(hmass : (1/2:ℝ) ≤ (stdGaussian (EuclideanSpace ℝ (Fin m))).real K)
(u : EuclideanSpace ℝ (Fin m)) (hu : ‖u‖ ≤ (1/5:ℝ))
(hcounter : (stdGaussian (EuclideanSpace ℝ (Fin m))).real (Komlos.closedConvexFold m K u) <
(stdGaussian (EuclideanSpace ℝ (Fin m))).real K) :
∃ w d b r : ℝ, 0 ≤ w ∧ 0 ≤ d ∧ 0 ≤ b ∧ 0 ≤ r ∧ r ≤ 1/5 ∧
tail (b * d) ≤ 2 * intervalMass r 0 ∧
(∫ x in Ioi (0 : ℝ), kernel (w - x) * intervalMass r (b * x)) +
(∫ x in (0 : ℝ)..d, kernel (w + x) * intervalMass r (-b * x)) <
∫ x in Ioi (0 : ℝ), kernel (w + d + x) * tail (b * (d + x)) := by sorry
Source
Shashwat Garg, Algorithms for Combinatorial Discrepancy, TU Eindhoven PhD thesis (2018), Chapter 3, Section 3.2.1. https://pure.tue.nl/ws/files/107722737/20181010_Garg.pdf . Proof of Theorem 15, Steps 1-3, printed pp. 19-22, in particular Lemmas 16-18 and the setup of Lemma 19. This is the counterexample form of those reductions, with b=-k, x*=w+d, and r=norm(u). The closed-fold formulation only enlarges the geometric fold. A proof must also discharge m=0, m=1, u=0, endpoint/empty slices, and limiting cases; these are not removed by extra hypotheses.