Frontier Distance Bounded by Directional Width
ProvedFunctionalInequalities.Analysis.infDist_frontier_le_of_forall_inner_le_readablefunctional-inequalitieskls-conjecturekls-modularkls-readablelog-concave
This proves that the distance to the frontier is bounded by a one-sided width.
Let be a normed real inner-product space, let be open and bounded, let , and let be a unit vector. If for some ,
then
The declaration is infDist_frontier_le_of_forall_inner_le_readable. No finite-dimensionality or convexity assumption appears in the statement.
Original Lean declaration header
Within namespace FunctionalInequalities.Analysis. Shown in its original module context. Proof omitted.
theorem infDist_frontier_le_of_forall_inner_le {E : Type*} [NormedAddCommGroup E]
[InnerProductSpace ℝ E] {S : Set E} (hS : IsOpen S) (hb : Bornology.IsBounded S)
{x : E} (hx : x ∈ S) {θ : E} (hθ : ‖θ‖ = 1) {t : ℝ} (ht : ∀ y ∈ S, ⟪y - x, θ⟫ ≤ t) :
infDist x (frontier S) ≤ t
Module navigation
Module: Analysis/Convex/Alexandrov.
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Source excerpt, lines 1687–1690 · Frozen source 408915f.
Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Analysis_Convex_Alexandrov open Set Metric open scoped RealInnerProductSpace
Formal statement
theorem FunctionalInequalities.Analysis.infDist_frontier_le_of_forall_inner_le_readable {E : Type*} [NormedAddCommGroup E]
[InnerProductSpace ℝ E] {S : Set E} (hS : IsOpen S) (hb : Bornology.IsBounded S)
{x : E} (hx : x ∈ S) {θ : E} (hθ : ‖θ‖ = 1) {t : ℝ} (ht : ∀ y ∈ S, ⟪y - x, θ⟫ ≤ t) :
infDist x (frontier S) ≤ t := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Analysis/Convex/Alexandrov.lean#L1685-L1740 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Analysis.Convex.Alexandrov`, declaration `FunctionalInequalities.Analysis.infDist_frontier_le_of_forall_inner_le`; the repository is private at upload time).