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Project-declaredLean 4.33.0-rc1 · mathlib@79d0395a1825

Second estimate

second_estimate

Plain-language statement

The second information estimate for τ\tau-minimizers. Let X1,X2X_1',X_2' be independent copies of X1,X2X_1,X_2, set k=d[X1;X2]k=d[X_1;X_2], I1=I[X1+X2:X1+X2X1+X2+X1+X2]I_1=I[X_1+X_2:X_1'+X_2\mid X_1+X_2+X_1'+X_2'], and I2=I[X1+X2:X1+X1X1+X2+X1+X2]I_2=I[X_1+X_2:X_1'+X_1\mid X_1+X_2+X_1'+X_2']. Then I22ηk+2η(2ηkI1)1ηI_2\le2\eta k+\dfrac{2\eta(2\eta k-I_1)}{1-\eta}.

Exact Lean statement

lemma second_estimate : I₂ ≤ 2 * p.η * k + (2 * p.η * (2 * p.η * k - I₁)) / (1 - p.η)

Formal artifact

Lean source

Canonical source
Full Lean sourceLean 4
lemma second_estimate : I₂  2 * p.η * k + (2 * p.η * (2 * p.η * k - I₁)) / (1 - p.η) := by  have hX₁_indep : IndepFun X₁ X₁' (μ := ℙ) := h_indep.indepFun (show 0  2 by decide)  have hX₂_indep : IndepFun X₂ X₂' (μ := ℙ) := h_indep.indepFun (show 1  3 by decide)  let Y : Fin 4  Ω  G := ![X₂, X₁, X₂', X₁']  have hY :  i, Measurable (Y i) := fun i => by fin_cases i <;> assumption  have hY_indep : iIndepFun Y := by exact h_indep.reindex_four_badc  have h := sum_of_rdist_eq_char_2 Y hY_indep hY  rw [show Y 0 = X₂ by rfl, show Y 1 = X₁ by rfl, show Y 2 = X₂' by rfl, show Y 3 = X₁' by rfl] at h  rw [ h₂.rdist_congr h₁, rdist_symm, rdist_symm (X := X₂ + X₂'),    condRuzsaDist_symm (Z := X₂ + X₂') (W := X₁ + X₁') (hX₂.add hX₂') (hX₁.add hX₁'),     two_mul] at h  replace h : 2 * k = d[X₁ + X₁' # X₂ + X₂'] + d[X₁ | X₁ + X₁' # X₂ | X₂ + X₂']      + I[X₁ + X₂ : X₁ + X₁'|X₁ + X₂ + X₁' + X₂'] := by    convert h using 3 <;> abel  have h' := condRuzsaDistance_ge_of_min p h_min hX₁ hX₂ (X₁ + X₁') (X₂ + X₂') (hX₁.add hX₁')    (hX₂.add hX₂')  have h₁' := condRuzsaDist_diff_le''' ℙ p.hmeas1 hX₁ hX₁' hX₁_indep  have h₂' := condRuzsaDist_diff_le''' ℙ p.hmeas2 hX₂ hX₂' hX₂_indep  rw [h₁.entropy_congr, add_sub_cancel_right,     (IdentDistrib.refl hX₁.aemeasurable).rdist_congr h₁] at h₁'  rw [h₂.entropy_congr, add_sub_cancel_right,     (IdentDistrib.refl hX₂.aemeasurable).rdist_congr h₂] at h₂'  have h'' : I₂  p.η * (d[X₁ # X₁] + d[X₂ # X₂]) := by    simp_rw [ add_comm X₁ X₁']    have h₁'' := mul_le_mul_of_nonneg_left h₁' (show 0  p.η by linarith [p.hη])    have h₂'' := mul_le_mul_of_nonneg_left h₂' (show 0  p.η by linarith [p.hη])    have := rdist_of_sums_ge' p _ _ _ _ hX₁ hX₂ hX₁' hX₂' h₁ h₂ h_indep h_min    linarith  nth_rewrite 1 [mul_div_assoc,  mul_add, mul_assoc, mul_left_comm]  refine h''.trans (mul_le_mul_of_nonneg_left ?_ (show 0  p.η by linarith [p.hη]))  exact second_estimate_aux p X₁ X₂ X₁' X₂' hX₁ hX₂ hX₁' hX₂' h₁ h₂ h_indep h_min
Project
Polynomial Freiman-Ruzsa project
License
Apache-2.0
Commit
a177b2e4abe4
Source
PFR/SecondEstimate.lean:103-133

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Project documentation

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Project-declaredLean 4.33.0-rc1

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Plain-language statement

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Source project: Polynomial Freiman-Ruzsa project

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Project-declaredLean 4.33.0-rc1

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better_PFR_conjecture'

Project documentation

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