Tilted Haar Laws Have Full Support
ProvedFunctionalInequalities.KLS.LogConcave.interior_support_nonempty_of_eq_tilted_readableThis theorem states that a probability law obtained by tilting Haar measure has nonempty support interior.
Let be a measurable normed space, let be an additive Haar measure, let be a probability measure, and let satisfy
Then
In fact, the support is all of under the hypotheses in the type. This auxiliary fact is used to supply the nonempty-interior-support assumption required by the closed weighted-gradient constructions.
Original Lean declaration header
Within namespace FunctionalInequalities.KLS.LogConcave. Shown in its original module context. Proof omitted.
theorem interior_support_nonempty_of_eq_tilted (m : Measure V) [m.IsAddHaarMeasure] {W : V → ℝ}
(hνW : (ν : Measure V) = m.tilted (-W)) :
(interior (ν : Measure V).support).Nonempty
Module navigation
Module: KLS/LogConcave/Density/Tilted.
Supporting theorems: integrable_exp_neg_of_eq_tilted.
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Source excerpt, lines 152–154 · Frozen source 408915f.
import Definitions.Def_KLSModular_FunctionalInequalities_KLS_LogConcave_Density_Tilted
open MeasureTheory Set
variable {V : Type*} [NormedAddCommGroup V] [InnerProductSpace ℝ V] [FiniteDimensional ℝ V]
[MeasurableSpace V] [BorelSpace V] {ν : MeasureTheory.ProbabilityMeasure V}
omit [InnerProductSpace ℝ V] [FiniteDimensional ℝ V] [BorelSpace V]
theorem FunctionalInequalities.KLS.LogConcave.interior_support_nonempty_of_eq_tilted_readable (m : Measure V) [m.IsAddHaarMeasure] {W : V → ℝ}
(hνW : (ν : Measure V) = m.tilted (-W)) :
(interior (ν : Measure V).support).Nonempty := by sorry