Ordered influence weights in the sharp correlation inequality #
This file defines the left-hand side 𝒲(f,g) of Theorem 1.2 and proves the
comparison (16) from Lemma 3.3. It also records the variance argument in
Corollary 1.3. The parameter optimization is in Chvatal.Optimization.
The largest influence over a Fourier index, as in Theorem 1.2. The empty index is assigned zero so that sums can run over the entire cube.
Equations
- Chvatal.maxInfluence f S = if h : S.Nonempty then S.sup' h (Chvatal.influence f) else 0
Instances For
The empty Fourier index makes no contribution to Theorem 1.2.
On a nonempty index, the influence weight is the ordinary finite maximum.
Every coordinate in an index is bounded by its maximum influence.
The spectral weights in Theorem 1.2 are nonnegative.
Lemma 3.3, equation (15): maximum influence is bounded by the odd-intersection Fourier energy. The formula also holds for the empty index under our zero convention.
The spectral expression 𝒲(f,g) in Theorem 1.2 and Section 5.
Its empty-index term vanishes by maxInfluence_empty.
Equations
- Chvatal.spectralWeight f g = ∑ S : Finset ι, Chvatal.fourier g S ^ 2 * Chvatal.maxInfluence f S
Instances For
The expression spectralWeight agrees with the paper's sum over nonempty indices.
Nonnegativity of the left side of Theorem 1.2, used when a covariance vanishes.
Equation (16): multiply Lemma 3.3 by the squared Fourier coefficient and sum.
The Parseval computation in Corollary 1.3: nonconstant Fourier coefficients of an antipodal Boolean function have total squared mass one quarter.
The lower bound on 𝒲(f,g) used in Corollary 1.3. Any common lower bound
on the coordinate influences is weighted by the variance 1/4.