Equal Fiber Cardinalities for Block Images
ProvedFunctionalInequalities.Analysis.Tensor.card_fiber_eq_readablefunctional-inequalitieskls-conjecturekls-modularkls-readablelog-concave
This is a finite-combinatorial fiber-counting lemma.
Let be finite and let be finite subsets. Assume that and are both -element subsets of . Then the two fibers of the map , restricted to permutations fixing every point outside , have equal cardinality:
This uniformity is used when grouping permutation sums by the image of .
Original Lean declaration header
Within namespace FunctionalInequalities.Analysis.Tensor. Shown in its original module context. Proof omitted.
theorem card_fiber_eq {A Q : Finset Ω} {F F' : Finset Ω}
(hF : F ∈ Q.powersetCard A.card) (hF' : F' ∈ Q.powersetCard A.card) :
((fixingOutside Q).filter fun σ : Equiv.Perm Ω => A.image σ = F).card =
((fixingOutside Q).filter fun σ : Equiv.Perm Ω => A.image σ = F').card
Module navigation
Module: Analysis/Tensor/Symmetrization.
Supporting theorems: exists_mem_fixingOutside_image_eq.
Previous: image_mem_powersetCard_of_mem_fixingOutside · Next: sum_fixingOutside_eq_smul_sum.
Source excerpt, lines 771–774 · Frozen source 408915f.
Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Analysis_Tensor_Symmetrization
variable {Ω : Type*} [Fintype Ω] [DecidableEq Ω]
Formal statement
theorem FunctionalInequalities.Analysis.Tensor.card_fiber_eq_readable {A Q : Finset Ω} {F F' : Finset Ω}
(hF : F ∈ Q.powersetCard A.card) (hF' : F' ∈ Q.powersetCard A.card) :
((fixingOutside Q).filter fun σ : Equiv.Perm Ω => A.image σ = F).card =
((fixingOutside Q).filter fun σ : Equiv.Perm Ω => A.image σ = F').card := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Analysis/Tensor/Symmetrization.lean#L769-L804 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Analysis.Tensor.Symmetrization`, declaration `FunctionalInequalities.Analysis.Tensor.card_fiber_eq`; the repository is private at upload time).