import Mathlib.LinearAlgebra.Eigenspace.Basic import Mathlib.LinearAlgebra.Matrix.ToLin import Mathlib.LinearAlgebra.Matrix.Symmetric import Mathlib.Analysis.Real.Sqrt import Mathlib.Tactic.FinCases import Mathlib.Tactic.NormNum import Mathlib.Tactic.Linarith import Mathlib.Tactic.Positivity import Mathlib.Tactic.Ring namespace OAI /-! # A finite factorial-lift counterexample The ten labels are the first cell's rows 0, 1, 2 and columns 3, 4, 5, and the second cell's rows 6, 7 and columns 8, 9. Conditioning on the first three rows gives a symmetric addition matrix with two distinct positive eigenvalues. This finite counterexample does not formalize a randomized approximation algorithm for contingency tables. -/ noncomputable section namespace DistinctLifts abbrev Label := Fin 10 abbrev Remaining := Fin 7 def count (S : Finset Label) (lo hi : ℕ) : ℕ := (S.filter fun i => lo ≤ i.val ∧ i.val < hi).card /-- The factorial lift for capacities 3 and 2, with constant weight one. It is defined on all sets here and subsequently restricted to 5-subsets. -/ def factorialLift (S : Finset Label) : ℝ := ((count S 0 3).factorial : ℝ) * (count S 3 6).factorial / (3 : ℕ).factorial * (((count S 6 8).factorial : ℝ) * (count S 8 10).factorial / (2 : ℕ).factorial) def fixed : Finset Label := {0, 1, 2} def remaining (i : Remaining) : Label := ⟨i.val + 3, by exact Nat.add_lt_add_right i.isLt 3⟩ def conditioned (i j : Remaining) : Finset Label := fixed ∪ {remaining i, remaining j} theorem conditioned_card (i j : Remaining) (h : i ≠ j) : (conditioned i j).card = 5 := by exact (by decide : ∀ i j : Remaining, i ≠ j → (conditioned i j).card = 5) i j h /-- Actual row-conditioned addition matrix, with the required zero diagonal. -/ def additionMatrix : Matrix Remaining Remaining ℝ := fun i j => if i = j then 0 else factorialLift (conditioned i j) /-- First block: three first-cell columns. Second block: two rows and two columns of the second cell; same-kind and mixed-kind entries differ. -/ def explicitMatrix : Matrix Remaining Remaining ℝ := fun i j => if i = j then 0 else if (i.val < 3 ∧ j.val < 3) ∨ (3 ≤ i.val ∧ i.val < 5 ∧ 3 ≤ j.val ∧ j.val < 5) ∨ (5 ≤ i.val ∧ 5 ≤ j.val) then 1 else 1 / 2 theorem additionMatrix_eq : additionMatrix = explicitMatrix := by have hcard : ({0, 1, 2} : Finset Label).card = 3 := by decide ext i j fin_cases i <;> fin_cases j <;> norm_num [additionMatrix, explicitMatrix, factorialLift, count, conditioned, fixed, remaining, Finset.filter_insert, Finset.filter_singleton, Finset.card_insert_of_notMem, Nat.factorial_succ, hcard] theorem additionMatrix_symmetric : Matrix.IsSymm additionMatrix := by rw [additionMatrix_eq] ext i j fin_cases i <;> fin_cases j <;> norm_num [explicitMatrix, Matrix.transpose_apply] /-- Both signs use the same three nonzero first coordinates. -/ def eigenvector (s : ℝ) : Remaining → ℝ := fun i => if i.val < 3 then 2 else s theorem eigenvector_nonzero (s : ℝ) : eigenvector s ≠ 0 := by intro h have := congrFun h (0 : Remaining) norm_num [eigenvector] at this /-- The calculation retains all four second-block entries separately. -/ theorem eigenvector_equation (s : ℝ) (hs : s ^ 2 = 3) : additionMatrix.mulVecLin (eigenvector s) = (2 + s) • eigenvector s := by rw [additionMatrix_eq] ext i fin_cases i <;> simp [Matrix.mulVec, dotProduct, Fin.sum_univ_succ, explicitMatrix, eigenvector] <;> nlinarith theorem eigenvalue_of_square (s : ℝ) (hs : s ^ 2 = 3) : Module.End.HasEigenvalue additionMatrix.mulVecLin (2 + s) := by apply Module.End.hasEigenvalue_of_hasEigenvector rw [Module.End.hasEigenvector_iff, Module.End.mem_eigenspace_iff] exact ⟨eigenvector_equation s hs, eigenvector_nonzero s⟩ /-- A necessary condition for having at most one positive eigenvalue, even when multiplicities are counted. Two distinct positive native eigenvalues already contradict this condition, so no inertia convention is required. -/ def PositiveEigenvaluesUnique (A : Matrix Remaining Remaining ℝ) : Prop := ∀ a b : ℝ, 0 < a → 0 < b → Module.End.HasEigenvalue A.mulVecLin a → Module.End.HasEigenvalue A.mulVecLin b → a = b theorem two_distinct_positive_eigenvalues : 0 < 2 + Real.sqrt 3 ∧ 0 < 2 - Real.sqrt 3 ∧ 2 + Real.sqrt 3 ≠ 2 - Real.sqrt 3 ∧ Module.End.HasEigenvalue additionMatrix.mulVecLin (2 + Real.sqrt 3) ∧ Module.End.HasEigenvalue additionMatrix.mulVecLin (2 - Real.sqrt 3) := by have hs : Real.sqrt 3 ^ 2 = 3 := Real.sq_sqrt (by norm_num) have hn := Real.sqrt_nonneg (3 : ℝ) have hp : 0 < Real.sqrt (3 : ℝ) := Real.sqrt_pos.2 (by norm_num) refine ⟨by positivity, by nlinarith, by nlinarith, eigenvalue_of_square _ hs, ?_⟩ have hm : (-Real.sqrt (3 : ℝ)) ^ 2 = 3 := by nlinarith simpa only [sub_eq_add_neg] using eigenvalue_of_square _ hm /-- The actual conditioned factorial lift fails the necessary spectral condition in the addition property. No claim about the other lift is made. -/ theorem factorial_lift_fails_addition_signature : ¬ PositiveEigenvaluesUnique additionMatrix := by obtain ⟨hp, hm, hne, hep, hem⟩ := two_distinct_positive_eigenvalues intro h exact hne (h _ _ hp hm hep hem) /-- Vectors constant on the groups of three and four remaining labels. -/ def blockVector (a b : ℝ) : Remaining → ℝ := fun i => if i.val < 3 then a else b theorem restricted_quadratic (a b : ℝ) : dotProduct (blockVector a b) (additionMatrix.mulVec (blockVector a b)) = 6 * a ^ 2 + 12 * a * b + 8 * b ^ 2 := by rw [additionMatrix_eq] simp [Matrix.mulVec, dotProduct, Fin.sum_univ_succ, explicitMatrix, blockVector] ring /-- Full finite counterexample: the conditioned sets have the required size, the actual factorial-lift matrix is symmetric, and it has two distinct positive eigenvalues in the native linear-map API. -/ theorem factorial_lift_counterexample : (∀ i j : Remaining, i ≠ j → (conditioned i j).card = 5) ∧ Matrix.IsSymm additionMatrix ∧ 0 < 2 + Real.sqrt 3 ∧ 0 < 2 - Real.sqrt 3 ∧ 2 + Real.sqrt 3 ≠ 2 - Real.sqrt 3 ∧ Module.End.HasEigenvalue additionMatrix.mulVecLin (2 + Real.sqrt 3) ∧ Module.End.HasEigenvalue additionMatrix.mulVecLin (2 - Real.sqrt 3) := by exact ⟨conditioned_card, additionMatrix_symmetric, two_distinct_positive_eigenvalues⟩ end DistinctLifts end end OAI