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LeanPool.AaronsonAmbainis.MainTheorem

The theorems for functions of bounded degree, from the links #

A function f on the cube with |f| ≤ 1 and degree at most d has a row of cost at most π² d² at every point (RowBoundPM, Theorem 6.8 of the paper), and ∑_j |f(x) - f(x^j)| ≤ 4 d² at every point x (SensitivityBound, Corollary 6.10). With these two inputs:

The main theorem (Theorem 1.1 of the paper) from the bound for the influences (Theorem 8.3), the sensitivity bound (Corollary 6.10) and the row bound (Theorem 6.8).

The bound for the mean absolute increments of a bounded function of degree at most d (Corollary 7.11 of the paper), from the bound for functions with rows of cost at most K (Theorem 7.10) and the row bound (Theorem 6.8).

theorem AaronsonAmbainis.rows_of_rowBound (hRB : RowBound) {ι : Type} [Fintype ι] (d : ℕ) (f : Cube ι → ℝ) (hf : ∀ (x : Cube ι), |f x| ≤ 1) (hdeg : HasDegreeLE f d) :
∃ (a : Cube ι → ι → ℝ), (∀ (x : Cube ι) (i : ι), 0 ≤ a x i) ∧ (∀ (x : Cube ι), ∑ i : ι, a x i ≤ Real.pi ^ 2 * ↑d ^ 2 / 2) ∧ ∀ (x y : Cube ι), |f x - f y| ≤ ∑ i ∈ diffSet x y, a x i

The row bound of Agarwal and Ben-David (the hypothesis RowBound) for a function f with values in [-1,1]: applied to (1 + f)/2, with the weights doubled, it gives rows of f of cost at most π² d² / 2.

The main theorem with the constant that the row bound of Agarwal and Ben-David gives (Remark 8.4 of the paper), from the bound for the influences, the sensitivity bound and the hypothesis RowBound.