6.1. Notions of pseudorandomness
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FABL.pBiasedRandomFunctionPMF[complete] -
FABL.measure_pBiasedRandomFunction_fourierFailure_le[complete]
Proposition 6.1. Let n>1 and let
f:\{-1,1\}^n\to\{0,1\} be a p-biased random function: independently
for every x\in\{-1,1\}^n, f(x)=1 with probability p and f(x)=0
with probability 1-p. Except with probability at most 2^{-n}, both
\left|\widehat f(\varnothing)-p\right|
\le 2\sqrt n\,2^{-n/2}
and
\left|\widehat f(S)\right|
\le 2\sqrt n\,2^{-n/2}
\qquad\text{for every nonempty }S\subseteq[n]
hold simultaneously.
Lean code for Proposition6.1.1●2 declarations
Associated Lean declarations
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FABL.pBiasedRandomFunctionPMF[complete]
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FABL.measure_pBiasedRandomFunction_fourierFailure_le[complete]
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FABL.pBiasedRandomFunctionPMF[complete] -
FABL.measure_pBiasedRandomFunction_fourierFailure_le[complete]
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defdefined in FABL/Chapter06/Pseudorandomness/RandomFunctions.leancomplete
def FABL.pBiasedRandomFunctionPMF (n : ℕ) (p : ℝ) (hp : p ∈ Set.Icc 0 1) : PMF (FABL.ZeroOneFunction n)
def FABL.pBiasedRandomFunctionPMF (n : ℕ) (p : ℝ) (hp : p ∈ Set.Icc 0 1) : PMF (FABL.ZeroOneFunction n)
The `p`-biased random-function law, with independent values at every cube input.
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theoremdefined in FABL/Chapter06/Pseudorandomness/RandomFunctions.leancomplete
theorem FABL.measure_pBiasedRandomFunction_fourierFailure_le (n : ℕ) (hn : 1 < n) (p : ℝ) (hp : p ∈ Set.Icc 0 1) : (FABL.pBiasedRandomFunctionPMF n p hp).toMeasure.real {f | FABL.randomBooleanFourierThreshold n < |FABL.fourierCoeff f.toReal ∅ - p| ∨ ∃ S, S.Nonempty ∧ FABL.randomBooleanFourierThreshold n < |FABL.fourierCoeff f.toReal S|} ≤ 2 ^ (-↑n)
theorem FABL.measure_pBiasedRandomFunction_fourierFailure_le (n : ℕ) (hn : 1 < n) (p : ℝ) (hp : p ∈ Set.Icc 0 1) : (FABL.pBiasedRandomFunctionPMF n p hp).toMeasure.real {f | FABL.randomBooleanFourierThreshold n < |FABL.fourierCoeff f.toReal ∅ - p| ∨ ∃ S, S.Nonempty ∧ FABL.randomBooleanFourierThreshold n < |FABL.fourierCoeff f.toReal S|} ≤ 2 ^ (-↑n)
O'Donnell, Proposition 6.1: for a `p`-biased random zero-one function in dimension `n > 1`, the constant coefficient is close to `p` and every nonconstant coefficient is small, simultaneously except with probability at most `2⁻ⁿ`.
Definition 6.2. A function f:\{-1,1\}^n\to\mathbb R is
\epsilon-regular (also called \epsilon-uniform) if
|\widehat f(S)|\le\epsilon
\qquad\text{for every nonempty }S\subseteq[n].
Lean code for Definition6.1.2●1 definition
Associated Lean declarations
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FABL.IsFourierRegular[complete]
-
FABL.IsFourierRegular[complete]
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defdefined in FABL/Chapter06/Pseudorandomness/Regularity.leancomplete
def FABL.IsFourierRegular {n : ℕ} (ε : ℝ) (f : FABL.SignCube n → ℝ) : Prop
def FABL.IsFourierRegular {n : ℕ} (ε : ℝ) (f : FABL.SignCube n → ℝ) : Prop
A real-valued function is `ε`-regular when every nonconstant Fourier coefficient has absolute value at most `ε`.
Remark 6.3. Every f:\{-1,1\}^n\to\mathbb R is
\lVert f\rVert_1-regular. When f takes values in [-1,1], the
interesting range is therefore \epsilon\le1.
Lean code for Lemma6.1.3●1 theorem
Associated Lean declarations
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FABL.isFourierRegular_uniformLpNorm_one[complete]
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FABL.isFourierRegular_uniformLpNorm_one[complete]
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theoremdefined in FABL/Chapter06/Pseudorandomness/Regularity.leancomplete
theorem FABL.isFourierRegular_uniformLpNorm_one {n : ℕ} (f : FABL.SignCube n → ℝ) : FABL.IsFourierRegular (FABL.uniformLpNorm 1 f) f
theorem FABL.isFourierRegular_uniformLpNorm_one {n : ℕ} (f : FABL.SignCube n → ℝ) : FABL.IsFourierRegular (FABL.uniformLpNorm 1 f) f
Every function is regular with parameter its uniform `L¹` norm.
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FABL.measure_pBiasedRandomFunction_not_isFourierRegular_le[complete] -
FABL.isFourierRegular_zero_iff_exists_const[complete] -
FABL.isFourierRegular_setIndicator_binaryAffineSubspace_of_codimension[complete] -
FABL.isFourierRegular_innerProductModTwo_zeroOne[complete] -
FABL.isFourierRegular_completeQuadratic_zeroOne[complete] -
FABL.parityFunction_not_isFourierRegular_of_lt_one[complete] -
FABL.isFourierRegular_majority_odd[complete]
Example 6.4. A random p-biased function is
2\sqrt n\,2^{-n/2}-regular with probability at least 1-2^{-n}.
A function is 0-regular exactly when it is constant. If
A\subseteq\mathbb F_2^n is an affine subspace of codimension k, then
\mathbf1_A is 2^{-k}-regular. For even n, the inner-product-mod-2
and complete-quadratic functions
\operatorname{IP}_n,\operatorname{CQ}_n:\mathbb F_2^n\to\{0,1\} are
2^{-n/2-1}-regular. A nonconstant parity \chi_S is not
\epsilon-regular for any \epsilon<1, while for odd
n=2m+1, \operatorname{Maj}_{2m+1} is
1/\sqrt{2m+1}-regular.
Lean code for Lemma6.1.4●7 theorems
Associated Lean declarations
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FABL.measure_pBiasedRandomFunction_not_isFourierRegular_le[complete]
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FABL.isFourierRegular_zero_iff_exists_const[complete]
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FABL.isFourierRegular_setIndicator_binaryAffineSubspace_of_codimension[complete]
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FABL.isFourierRegular_innerProductModTwo_zeroOne[complete]
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FABL.isFourierRegular_completeQuadratic_zeroOne[complete]
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FABL.parityFunction_not_isFourierRegular_of_lt_one[complete]
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FABL.isFourierRegular_majority_odd[complete]
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FABL.measure_pBiasedRandomFunction_not_isFourierRegular_le[complete] -
FABL.isFourierRegular_zero_iff_exists_const[complete] -
FABL.isFourierRegular_setIndicator_binaryAffineSubspace_of_codimension[complete] -
FABL.isFourierRegular_innerProductModTwo_zeroOne[complete] -
FABL.isFourierRegular_completeQuadratic_zeroOne[complete] -
FABL.parityFunction_not_isFourierRegular_of_lt_one[complete] -
FABL.isFourierRegular_majority_odd[complete]
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theoremdefined in FABL/Chapter06/Pseudorandomness/RandomFunctions.leancomplete
theorem FABL.measure_pBiasedRandomFunction_not_isFourierRegular_le (n : ℕ) (hn : 1 < n) (p : ℝ) (hp : p ∈ Set.Icc 0 1) : (FABL.pBiasedRandomFunctionPMF n p hp).toMeasure.real {f | ¬FABL.IsFourierRegular (FABL.randomBooleanFourierThreshold n) f.toReal} ≤ 2 ^ (-↑n)
theorem FABL.measure_pBiasedRandomFunction_not_isFourierRegular_le (n : ℕ) (hn : 1 < n) (p : ℝ) (hp : p ∈ Set.Icc 0 1) : (FABL.pBiasedRandomFunctionPMF n p hp).toMeasure.real {f | ¬FABL.IsFourierRegular (FABL.randomBooleanFourierThreshold n) f.toReal} ≤ 2 ^ (-↑n)
O'Donnell, Example 6.4 (first assertion): a `p`-biased random function fails `2 √n · 2⁻ⁿᐟ²`-regularity with probability at most `2⁻ⁿ`.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Regularity.leancomplete
theorem FABL.isFourierRegular_zero_iff_exists_const {n : ℕ} (f : FABL.SignCube n → ℝ) : FABL.IsFourierRegular 0 f ↔ ∃ c, f = fun x => c
theorem FABL.isFourierRegular_zero_iff_exists_const {n : ℕ} (f : FABL.SignCube n → ℝ) : FABL.IsFourierRegular 0 f ↔ ∃ c, f = fun x => c
A function is zero-regular exactly when it is constant.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.isFourierRegular_setIndicator_binaryAffineSubspace_of_codimension {n : ℕ} (H : Submodule FABL.𝔽₂ (FABL.F₂Cube n)) (a : FABL.F₂Cube n) (k : ℕ) (hcodim : FABL.f₂Codimension H = k) : FABL.IsFourierRegular (2 ^ k)⁻¹ (FABL.binaryFunctionOnSignCube (FABL.setIndicator ↑(FABL.binaryAffineSubspace H a)))
theorem FABL.isFourierRegular_setIndicator_binaryAffineSubspace_of_codimension {n : ℕ} (H : Submodule FABL.𝔽₂ (FABL.F₂Cube n)) (a : FABL.F₂Cube n) (k : ℕ) (hcodim : FABL.f₂Codimension H = k) : FABL.IsFourierRegular (2 ^ k)⁻¹ (FABL.binaryFunctionOnSignCube (FABL.setIndicator ↑(FABL.binaryAffineSubspace H a)))
Example 6.4 in codimension form: an affine-subspace indicator of codimension `k` is `2⁻ᵏ`-regular.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.isFourierRegular_innerProductModTwo_zeroOne (m : ℕ) : FABL.IsFourierRegular (2 ^ (m + 1))⁻¹ (FABL.binaryFunctionOnSignCube (FABL.booleanRealEmbedding (FABL.booleanFunctionF₂Encoding (FABL.innerProductModTwoBoolean m))))
theorem FABL.isFourierRegular_innerProductModTwo_zeroOne (m : ℕ) : FABL.IsFourierRegular (2 ^ (m + 1))⁻¹ (FABL.binaryFunctionOnSignCube (FABL.booleanRealEmbedding (FABL.booleanFunctionF₂Encoding (FABL.innerProductModTwoBoolean m))))
The canonical zero-one encoding of `IP` on `2m` variables is `2⁻ᵐ⁻¹`-regular.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.isFourierRegular_completeQuadratic_zeroOne (n : ℕ) (hn : Even n) : FABL.IsFourierRegular (2 ^ (n / 2 + 1))⁻¹ (FABL.binaryFunctionOnSignCube (FABL.booleanRealEmbedding (FABL.booleanFunctionF₂Encoding (FABL.completeQuadraticBoolean n))))
theorem FABL.isFourierRegular_completeQuadratic_zeroOne (n : ℕ) (hn : Even n) : FABL.IsFourierRegular (2 ^ (n / 2 + 1))⁻¹ (FABL.binaryFunctionOnSignCube (FABL.booleanRealEmbedding (FABL.booleanFunctionF₂Encoding (FABL.completeQuadraticBoolean n))))
In even dimension, the canonical zero-one encoding of `CQ` is `2⁻ⁿᐟ²⁻¹`-regular.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.parityFunction_not_isFourierRegular_of_lt_one {n : ℕ} (S : Finset (Fin n)) (hS : S.Nonempty) {ε : ℝ} (hε : ε < 1) : ¬FABL.IsFourierRegular ε (FABL.parityFunction S).toReal
theorem FABL.parityFunction_not_isFourierRegular_of_lt_one {n : ℕ} (S : Finset (Fin n)) (hS : S.Nonempty) {ε : ℝ} (hε : ε < 1) : ¬FABL.IsFourierRegular ε (FABL.parityFunction S).toReal
A nonconstant parity is not `ε`-regular for any `ε < 1`.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.isFourierRegular_majority_odd (m : ℕ) : FABL.IsFourierRegular (1 / √↑(2 * m + 1)) (FABL.majority (2 * m + 1)).toReal
theorem FABL.isFourierRegular_majority_odd (m : ℕ) : FABL.IsFourierRegular (1 / √↑(2 * m + 1)) (FABL.majority (2 * m + 1)).toReal
Exercise 5.21 gives the `1 / √n` regularity assertion of Example 6.4 at every odd arity.
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FABL.ProbabilityDensity.IsBiased[complete] -
FABL.ProbabilityDensity.isBiased_iff_expectation[complete] -
FABL.coordinateProjectionLinear[complete] -
FABL.ProbabilityDensity.coordinateMarginal[complete] -
FABL.ProbabilityDensity.vectorFourierCoeff_coordinateMarginal[complete] -
FABL.ProbabilityDensity.IsBiased.coordinateMarginal[complete]
Definition 6.5. A probability density
\varphi:\mathbb F_2^n\to\mathbb R_{\ge0} is an
\epsilon-biased density if it is \epsilon-regular. Equivalently,
\left|\mathbb E_{\boldsymbol x\sim\varphi}[\chi_\gamma(\boldsymbol x)]\right|
\le\epsilon
\qquad\text{for every }\gamma\in\widehat{\mathbb F_2^n}\setminus\{0\}.
The marginal on every set of coordinates is again \epsilon-biased. If
\varphi=\varphi_A=\mathbf1_A/\mathbb E[\mathbf1_A] for
A\subseteq\mathbb F_2^n, then A is called an \epsilon-biased set.
Lean code for Definition6.1.5●6 declarations
Associated Lean declarations
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FABL.ProbabilityDensity.IsBiased[complete]
-
FABL.ProbabilityDensity.isBiased_iff_expectation[complete]
-
FABL.coordinateProjectionLinear[complete]
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FABL.ProbabilityDensity.coordinateMarginal[complete]
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FABL.ProbabilityDensity.vectorFourierCoeff_coordinateMarginal[complete]
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FABL.ProbabilityDensity.IsBiased.coordinateMarginal[complete]
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FABL.ProbabilityDensity.IsBiased[complete] -
FABL.ProbabilityDensity.isBiased_iff_expectation[complete] -
FABL.coordinateProjectionLinear[complete] -
FABL.ProbabilityDensity.coordinateMarginal[complete] -
FABL.ProbabilityDensity.vectorFourierCoeff_coordinateMarginal[complete] -
FABL.ProbabilityDensity.IsBiased.coordinateMarginal[complete]
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defdefined in FABL/Chapter06/Pseudorandomness/SmallBias.leancomplete
def FABL.ProbabilityDensity.IsBiased {n : ℕ} (φ : FABL.ProbabilityDensity n) (ε : ℝ) : Prop
def FABL.ProbabilityDensity.IsBiased {n : ℕ} (φ : FABL.ProbabilityDensity n) (ε : ℝ) : Prop
O'Donnell, Definition 6.5: a density is `ε`-biased when every nontrivial vector-indexed Fourier coefficient has absolute value at most `ε`.
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theoremdefined in FABL/Chapter06/Pseudorandomness/SmallBias.leancomplete
theorem FABL.ProbabilityDensity.isBiased_iff_expectation {n : ℕ} (φ : FABL.ProbabilityDensity n) (ε : ℝ) : φ.IsBiased ε ↔ ∀ (γ : FABL.F₂Cube n), γ ≠ 0 → |φ.expectation fun x => (FABL.vectorWalshCharacter γ) x| ≤ ε
theorem FABL.ProbabilityDensity.isBiased_iff_expectation {n : ℕ} (φ : FABL.ProbabilityDensity n) (ε : ℝ) : φ.IsBiased ε ↔ ∀ (γ : FABL.F₂Cube n), γ ≠ 0 → |φ.expectation fun x => (FABL.vectorWalshCharacter γ) x| ≤ ε
Small bias is equivalently a bound on every nontrivial parity expectation under the distribution induced by the density.
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defdefined in FABL/Chapter06/Pseudorandomness/SmallBiasMarginals.leancomplete
def FABL.coordinateProjectionLinear {n : ℕ} (J : Finset (Fin n)) : FABL.F₂Cube n →ₗ[FABL.𝔽₂] FABL.F₂Cube J.card
def FABL.coordinateProjectionLinear {n : ℕ} (J : Finset (Fin n)) : FABL.F₂Cube n →ₗ[FABL.𝔽₂] FABL.F₂Cube J.card
Restriction to the coordinates in `J`, canonically enumerated by `Fin J.card`.
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defdefined in FABL/Chapter06/Pseudorandomness/SmallBiasMarginals.leancomplete
def FABL.ProbabilityDensity.coordinateMarginal {n : ℕ} (φ : FABL.ProbabilityDensity n) (J : Finset (Fin n)) : FABL.ProbabilityDensity J.card
def FABL.ProbabilityDensity.coordinateMarginal {n : ℕ} (φ : FABL.ProbabilityDensity n) (J : Finset (Fin n)) : FABL.ProbabilityDensity J.card
The marginal density on the coordinates in `J`.
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theoremdefined in FABL/Chapter06/Pseudorandomness/SmallBiasMarginals.leancomplete
theorem FABL.ProbabilityDensity.vectorFourierCoeff_coordinateMarginal {n : ℕ} (φ : FABL.ProbabilityDensity n) (J : Finset (Fin n)) (γ : FABL.F₂Cube J.card) : FABL.vectorFourierCoeff (φ.coordinateMarginal J).toFun γ = FABL.vectorFourierCoeff φ.toFun (FABL.coordinateFrequencyLift J γ)
theorem FABL.ProbabilityDensity.vectorFourierCoeff_coordinateMarginal {n : ℕ} (φ : FABL.ProbabilityDensity n) (J : Finset (Fin n)) (γ : FABL.F₂Cube J.card) : FABL.vectorFourierCoeff (φ.coordinateMarginal J).toFun γ = FABL.vectorFourierCoeff φ.toFun (FABL.coordinateFrequencyLift J γ)
Fourier coefficients of a coordinate marginal are the source coefficients at the lifted frequency.
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theoremdefined in FABL/Chapter06/Pseudorandomness/SmallBiasMarginals.leancomplete
theorem FABL.ProbabilityDensity.IsBiased.coordinateMarginal {n : ℕ} {φ : FABL.ProbabilityDensity n} {ε : ℝ} (hφ : φ.IsBiased ε) (J : Finset (Fin n)) : (φ.coordinateMarginal J).IsBiased ε
theorem FABL.ProbabilityDensity.IsBiased.coordinateMarginal {n : ℕ} {φ : FABL.ProbabilityDensity n} {ε : ℝ} (hφ : φ.IsBiased ε) (J : Finset (Fin n)) : (φ.coordinateMarginal J).IsBiased ε
O'Donnell, Definition 6.5: every coordinate marginal of an `ε`-biased density is again `ε`-biased.
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FABL.ProbabilityDensity.isBiased_one[complete] -
FABL.ProbabilityDensity.isBiased_zero_iff_eq_uniform[complete] -
FABL.ProbabilityDensity.affineSubspaceDensity_not_isBiased_of_lt_one[complete] -
FABL.ProbabilityDensity.constantPairDensity_not_isBiased_of_two_le[complete] -
FABL.ProbabilityDensity.constantPairDensity_one_isBiased_zero[complete]
Example 6.6. Every probability density is 1-biased. The uniform density
\varphi\equiv1 on \mathbb F_2^n is the only 0-biased density. If
A is a proper affine subspace, then \varphi_A is not
\epsilon-biased for any \epsilon<1.
The book next prints that the two-point set
E=\{(0,\ldots,0),(1,\ldots,1)\} is 1/2-biased. This conflicts with
Definition 6.5 and with the preceding affine-subspace statement. The corrected
conclusion is: if n\ge2, choose a nonzero even-weight \gamma; then
\mathbb E_{x\sim\varphi_E}[\chi_\gamma(x)]=1,
so E is not \epsilon-biased for any \epsilon<1. For n=1,
E=\mathbb F_2 and its density is 0-biased.
Lean code for Lemma6.1.6●5 theorems
Associated Lean declarations
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FABL.ProbabilityDensity.isBiased_one[complete]
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FABL.ProbabilityDensity.isBiased_zero_iff_eq_uniform[complete]
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FABL.ProbabilityDensity.affineSubspaceDensity_not_isBiased_of_lt_one[complete]
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FABL.ProbabilityDensity.constantPairDensity_not_isBiased_of_two_le[complete]
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FABL.ProbabilityDensity.constantPairDensity_one_isBiased_zero[complete]
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FABL.ProbabilityDensity.isBiased_one[complete] -
FABL.ProbabilityDensity.isBiased_zero_iff_eq_uniform[complete] -
FABL.ProbabilityDensity.affineSubspaceDensity_not_isBiased_of_lt_one[complete] -
FABL.ProbabilityDensity.constantPairDensity_not_isBiased_of_two_le[complete] -
FABL.ProbabilityDensity.constantPairDensity_one_isBiased_zero[complete]
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theoremdefined in FABL/Chapter06/Pseudorandomness/SmallBias.leancomplete
theorem FABL.ProbabilityDensity.isBiased_one {n : ℕ} (φ : FABL.ProbabilityDensity n) : φ.IsBiased 1
theorem FABL.ProbabilityDensity.isBiased_one {n : ℕ} (φ : FABL.ProbabilityDensity n) : φ.IsBiased 1
O'Donnell, Example 6.6: every probability density is `1`-biased.
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theoremdefined in FABL/Chapter06/Pseudorandomness/SmallBias.leancomplete
theorem FABL.ProbabilityDensity.isBiased_zero_iff_eq_uniform {n : ℕ} (φ : FABL.ProbabilityDensity n) : φ.IsBiased 0 ↔ φ.toFun = fun x => 1
theorem FABL.ProbabilityDensity.isBiased_zero_iff_eq_uniform {n : ℕ} (φ : FABL.ProbabilityDensity n) : φ.IsBiased 0 ↔ φ.toFun = fun x => 1
O'Donnell, Example 6.6: the uniform density is the unique `0`-biased density.
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theoremdefined in FABL/Chapter06/Pseudorandomness/SmallBias.leancomplete
theorem FABL.ProbabilityDensity.affineSubspaceDensity_not_isBiased_of_lt_one {n : ℕ} (H : Submodule FABL.𝔽₂ (FABL.F₂Cube n)) (a : FABL.F₂Cube n) (hH : H ≠ ⊤) {ε : ℝ} (hε : ε < 1) : ¬(FABL.subsetDensity ↑(FABL.binaryAffineSubspace H a) ⋯).IsBiased ε
theorem FABL.ProbabilityDensity.affineSubspaceDensity_not_isBiased_of_lt_one {n : ℕ} (H : Submodule FABL.𝔽₂ (FABL.F₂Cube n)) (a : FABL.F₂Cube n) (hH : H ≠ ⊤) {ε : ℝ} (hε : ε < 1) : ¬(FABL.subsetDensity ↑(FABL.binaryAffineSubspace H a) ⋯).IsBiased ε
O'Donnell, Example 6.6: the density of a proper affine subspace is not `ε`-biased for any `ε < 1`.
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theoremdefined in FABL/Chapter06/Pseudorandomness/SmallBias.leancomplete
theorem FABL.ProbabilityDensity.constantPairDensity_not_isBiased_of_two_le {n : ℕ} (hn : 2 ≤ n) {ε : ℝ} (hε : ε < 1) : ¬(FABL.ProbabilityDensity.constantPairDensity n).IsBiased ε
theorem FABL.ProbabilityDensity.constantPairDensity_not_isBiased_of_two_le {n : ℕ} (hn : 2 ≤ n) {ε : ℝ} (hε : ε < 1) : ¬(FABL.ProbabilityDensity.constantPairDensity n).IsBiased ε
Corrected Example 6.6: for `n ≥ 2`, the all-zero/all-one pair is not `ε`-biased for any `ε < 1`.
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theoremdefined in FABL/Chapter06/Pseudorandomness/SmallBias.leancomplete
theorem FABL.ProbabilityDensity.constantPairDensity_one_isBiased_zero : (FABL.ProbabilityDensity.constantPairDensity 1).IsBiased 0
theorem FABL.ProbabilityDensity.constantPairDensity_one_isBiased_zero : (FABL.ProbabilityDensity.constantPairDensity 1).IsBiased 0
Corrected Example 6.6: in dimension one, the all-zero/all-one pair density is `0`-biased.
Proposition 6.7. Let f:\mathbb F_2^n\to\mathbb R.
(1) If f is \epsilon-regular, then
\lVert\widehat f\rVert_4^4-\mathbb E[f]^4
\le\epsilon^2\operatorname{Var}[f].
(2) If f is not \epsilon-regular, then
\lVert\widehat f\rVert_4^4-\mathbb E[f]^4\ge\epsilon^4.
Here
\lVert\widehat f\rVert_4^4
=\mathbb E_{x,y,z}
[f(x)f(y)f(z)f(x+y+z)].
Lean code for Proposition6.1.7●4 declarations
Associated Lean declarations
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defdefined in FABL/Chapter06/Pseudorandomness/FourierFourthMoment.leancomplete
def FABL.vectorFourierFourthMoment {n : ℕ} (f : FABL.F₂Cube n → ℝ) : ℝ
def FABL.vectorFourierFourthMoment {n : ℕ} (f : FABL.F₂Cube n → ℝ) : ℝ
The fourth power of the vector-indexed Fourier `4`-norm.
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theoremdefined in FABL/Chapter06/Pseudorandomness/FourierFourthMoment.leancomplete
theorem FABL.vectorFourierFourthMoment_sub_mean_pow_four_le {n : ℕ} (f : FABL.F₂Cube n → ℝ) {ε : ℝ} (hregular : FABL.IsFourierRegular ε (FABL.binaryFunctionOnSignCube f)) : FABL.vectorFourierFourthMoment f - FABL.mean f ^ 4 ≤ ε ^ 2 * FABL.variance f
theorem FABL.vectorFourierFourthMoment_sub_mean_pow_four_le {n : ℕ} (f : FABL.F₂Cube n → ℝ) {ε : ℝ} (hregular : FABL.IsFourierRegular ε (FABL.binaryFunctionOnSignCube f)) : FABL.vectorFourierFourthMoment f - FABL.mean f ^ 4 ≤ ε ^ 2 * FABL.variance f
O'Donnell, Proposition 6.7(1): regularity bounds the nonconstant fourth spectral moment by `ε²` times the variance.
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theoremdefined in FABL/Chapter06/Pseudorandomness/FourierFourthMoment.leancomplete
theorem FABL.epsilon_pow_four_le_vectorFourierFourthMoment_sub_mean_pow_four {n : ℕ} (f : FABL.F₂Cube n → ℝ) {ε : ℝ} (hε : 0 ≤ ε) (hregular : ¬FABL.IsFourierRegular ε (FABL.binaryFunctionOnSignCube f)) : ε ^ 4 ≤ FABL.vectorFourierFourthMoment f - FABL.mean f ^ 4
theorem FABL.epsilon_pow_four_le_vectorFourierFourthMoment_sub_mean_pow_four {n : ℕ} (f : FABL.F₂Cube n → ℝ) {ε : ℝ} (hε : 0 ≤ ε) (hregular : ¬FABL.IsFourierRegular ε (FABL.binaryFunctionOnSignCube f)) : ε ^ 4 ≤ FABL.vectorFourierFourthMoment f - FABL.mean f ^ 4
O'Donnell, Proposition 6.7(2): failure of `ε`-regularity forces at least `ε⁴` nonconstant fourth spectral mass.
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theoremdefined in FABL/Chapter06/Pseudorandomness/FourierFourthMoment.leancomplete
theorem FABL.vectorFourierFourthMoment_eq_additiveEnergy {n : ℕ} (f : FABL.F₂Cube n → ℝ) : FABL.vectorFourierFourthMoment f = Finset.univ.expect fun x => Finset.univ.expect fun y => Finset.univ.expect fun z => f x * f y * f z * f (x + y + z)
theorem FABL.vectorFourierFourthMoment_eq_additiveEnergy {n : ℕ} (f : FABL.F₂Cube n → ℝ) : FABL.vectorFourierFourthMoment f = Finset.univ.expect fun x => Finset.univ.expect fun y => Finset.univ.expect fun z => f x * f y * f z * f (x + y + z)
O'Donnell, Proposition 6.7: the exact additive-energy formula for the fourth spectral moment.
Fact 6.8. Fix \delta\in[0,1] and choose
f:\{-1,1\}^n\to\{-1,1\} uniformly at random. For every coordinate
i\in[n],
\mathbb E_f\!\left[\operatorname{Inf}^{(1-\delta)}_i[f]\right]
=\frac{(1-\delta/2)^n}{2-\delta}.
Lean code for Lemma6.1.8●1 theorem
Associated Lean declarations
-
theoremdefined in FABL/Chapter06/Pseudorandomness/RandomFunctions.leancomplete
theorem FABL.pmfExpectation_uniformBooleanFunction_stableInfluence_one_sub {n : ℕ} (δ : ℝ) (hδ : δ ∈ Set.Icc 0 1) (i : Fin n) : (FABL.pmfExpectation (FABL.uniformPMF (FABL.BooleanFunction n)) fun f => FABL.stableInfluence (1 - δ) f.toReal i) = (1 - δ / 2) ^ n / (2 - δ)
theorem FABL.pmfExpectation_uniformBooleanFunction_stableInfluence_one_sub {n : ℕ} (δ : ℝ) (hδ : δ ∈ Set.Icc 0 1) (i : Fin n) : (FABL.pmfExpectation (FABL.uniformPMF (FABL.BooleanFunction n)) fun f => FABL.stableInfluence (1 - δ) f.toReal i) = (1 - δ / 2) ^ n / (2 - δ)
O'Donnell, Fact 6.8 and Exercise 6.2: for a uniformly random sign-valued function, the expected `(1 - δ)`-stable influence of every coordinate is `(1 - δ / 2)ⁿ / (2 - δ)`.
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FABL.HasSmallStableInfluences[complete] -
FABL.HasSmallInfluences[complete] -
FABL.stableInfluence_one_eq_influence[complete] -
FABL.hasSmallInfluences_iff[complete]
Definition 6.9. A function f:\{-1,1\}^n\to\mathbb R has
(\epsilon,\delta)-small stable influences, or has no
(\epsilon,\delta)-notable coordinates, if
\operatorname{Inf}^{(1-\delta)}_i[f]\le\epsilon
\qquad\text{for every }i\in[n].
This property gets stronger as \epsilon and \delta decrease. When
\delta=0, it is called having \epsilon-small influences.
Lean code for Definition6.1.9●4 declarations
Associated Lean declarations
-
FABL.HasSmallStableInfluences[complete]
-
FABL.HasSmallInfluences[complete]
-
FABL.stableInfluence_one_eq_influence[complete]
-
FABL.hasSmallInfluences_iff[complete]
-
FABL.HasSmallStableInfluences[complete] -
FABL.HasSmallInfluences[complete] -
FABL.stableInfluence_one_eq_influence[complete] -
FABL.hasSmallInfluences_iff[complete]
-
defdefined in FABL/Chapter06/Pseudorandomness/StableInfluences.leancomplete
def FABL.HasSmallStableInfluences {n : ℕ} (ε δ : ℝ) (f : FABL.SignCube n → ℝ) : Prop
def FABL.HasSmallStableInfluences {n : ℕ} (ε δ : ℝ) (f : FABL.SignCube n → ℝ) : Prop
A function has `(ε, δ)`-small stable influences when every coordinate has stable influence at correlation `1 - δ` at most `ε`.
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defdefined in FABL/Chapter06/Pseudorandomness/StableInfluences.leancomplete
def FABL.HasSmallInfluences {n : ℕ} (ε : ℝ) (f : FABL.SignCube n → ℝ) : Prop
def FABL.HasSmallInfluences {n : ℕ} (ε : ℝ) (f : FABL.SignCube n → ℝ) : Prop
The specialization called `ε`-small influences in the book.
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theoremdefined in FABL/Chapter06/Pseudorandomness/StableInfluences.leancomplete
theorem FABL.stableInfluence_one_eq_influence {n : ℕ} (f : FABL.SignCube n → ℝ) (i : Fin n) : FABL.stableInfluence 1 f i = FABL.influence f i
theorem FABL.stableInfluence_one_eq_influence {n : ℕ} (f : FABL.SignCube n → ℝ) (i : Fin n) : FABL.stableInfluence 1 f i = FABL.influence f i
At correlation one, stable influence is ordinary influence.
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theoremdefined in FABL/Chapter06/Pseudorandomness/StableInfluences.leancomplete
theorem FABL.hasSmallInfluences_iff {n : ℕ} (ε : ℝ) (f : FABL.SignCube n → ℝ) : FABL.HasSmallInfluences ε f ↔ ∀ (i : Fin n), FABL.influence f i ≤ ε
theorem FABL.hasSmallInfluences_iff {n : ℕ} (ε : ℝ) (f : FABL.SignCube n → ℝ) : FABL.HasSmallInfluences ε f ↔ ∀ (i : Fin n), FABL.influence f i ≤ ε
Small influences are exactly coordinatewise bounds on ordinary influences.
-
FABL.const_hasSmallStableInfluences_zero_zero[complete] -
FABL.hasSmallInfluences_zero_iff_exists_const[complete] -
FABL.majority_hasSmallInfluences[complete] -
FABL.stableInfluence_parityFunction[complete] -
FABL.parityFunction_hasSmallStableInfluences_of_log_bound[complete] -
FABL.exists_stableInfluence_ge_of_isKJunta_of_balanced[complete] -
FABL.not_hasSmallStableInfluences_of_isKJunta_of_balanced[complete] -
FABL.leadingCoordinateTimes[complete] -
FABL.one_sub_sqrt_le_stableInfluence_leadingCoordinateTimes_majority_odd[complete]
Example 6.10. Constants have (0,0)-small stable influences, and they are
the only functions with 0-small influences. Majority has
1/\sqrt n-small influences. For a nonempty parity \chi_S,
\operatorname{Inf}^{(1-\delta)}_i[\chi_S]
=
\begin{cases}
(1-\delta)^{|S|-1},&i\in S,\\
0,&i\notin S;
\end{cases}
hence it has (\epsilon,\delta)-small stable influences whenever
|S|\ge\ln(e/\epsilon)/\delta.
If an unbiased k-junta f has variance 1, then some coordinate has
stable influence at least (1-\delta)^{k-1}/k; thus it does not have
((1-\delta)^k/k,\delta)-small stable influences for
\delta\in(0,1). Finally,
For odd n=2m+1, the function
f(x_0,x_1,\ldots,x_n)=x_0\operatorname{Maj}_n(x_1,\ldots,x_n)
satisfies
\operatorname{Inf}^{(1-\delta)}_0[f]\ge1-\sqrt\delta.
Lean code for Lemma6.1.10●9 declarations
Associated Lean declarations
-
FABL.const_hasSmallStableInfluences_zero_zero[complete]
-
FABL.hasSmallInfluences_zero_iff_exists_const[complete]
-
FABL.majority_hasSmallInfluences[complete]
-
FABL.stableInfluence_parityFunction[complete]
-
FABL.parityFunction_hasSmallStableInfluences_of_log_bound[complete]
-
FABL.exists_stableInfluence_ge_of_isKJunta_of_balanced[complete]
-
FABL.not_hasSmallStableInfluences_of_isKJunta_of_balanced[complete]
-
FABL.leadingCoordinateTimes[complete]
-
FABL.one_sub_sqrt_le_stableInfluence_leadingCoordinateTimes_majority_odd[complete]
-
FABL.const_hasSmallStableInfluences_zero_zero[complete] -
FABL.hasSmallInfluences_zero_iff_exists_const[complete] -
FABL.majority_hasSmallInfluences[complete] -
FABL.stableInfluence_parityFunction[complete] -
FABL.parityFunction_hasSmallStableInfluences_of_log_bound[complete] -
FABL.exists_stableInfluence_ge_of_isKJunta_of_balanced[complete] -
FABL.not_hasSmallStableInfluences_of_isKJunta_of_balanced[complete] -
FABL.leadingCoordinateTimes[complete] -
FABL.one_sub_sqrt_le_stableInfluence_leadingCoordinateTimes_majority_odd[complete]
-
theoremdefined in FABL/Chapter06/Pseudorandomness/StableInfluences.leancomplete
theorem FABL.const_hasSmallStableInfluences_zero_zero {n : ℕ} (c : ℝ) : FABL.HasSmallStableInfluences 0 0 fun x => c
theorem FABL.const_hasSmallStableInfluences_zero_zero {n : ℕ} (c : ℝ) : FABL.HasSmallStableInfluences 0 0 fun x => c
Constant functions have `(0, 0)`-small stable influences.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.hasSmallInfluences_zero_iff_exists_const {n : ℕ} (f : FABL.SignCube n → ℝ) : FABL.HasSmallInfluences 0 f ↔ ∃ c, f = fun x => c
theorem FABL.hasSmallInfluences_zero_iff_exists_const {n : ℕ} (f : FABL.SignCube n → ℝ) : FABL.HasSmallInfluences 0 f ↔ ∃ c, f = fun x => c
A real-valued function has zero-small influences exactly when it is constant.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.majority_hasSmallInfluences (n : ℕ) : FABL.HasSmallInfluences (1 / √↑n) (FABL.majority n).toReal
theorem FABL.majority_hasSmallInfluences (n : ℕ) : FABL.HasSmallInfluences (1 / √↑n) (FABL.majority n).toReal
Majority has `1 / √n`-small influences in every dimension.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.stableInfluence_parityFunction {n : ℕ} (ρ : ℝ) (S : Finset (Fin n)) (i : Fin n) : FABL.stableInfluence ρ (FABL.parityFunction S).toReal i = if i ∈ S then ρ ^ (S.card - 1) else 0
theorem FABL.stableInfluence_parityFunction {n : ℕ} (ρ : ℝ) (S : Finset (Fin n)) (i : Fin n) : FABL.stableInfluence ρ (FABL.parityFunction S).toReal i = if i ∈ S then ρ ^ (S.card - 1) else 0
The exact stable-influence profile of a parity.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.parityFunction_hasSmallStableInfluences_of_log_bound {n : ℕ} (S : Finset (Fin n)) (ε δ : ℝ) (hε : 0 < ε) (hδ : δ ∈ Set.Ioc 0 1) (hcard : Real.log (Real.exp 1 / ε) / δ ≤ ↑S.card) : FABL.HasSmallStableInfluences ε δ (FABL.parityFunction S).toReal
theorem FABL.parityFunction_hasSmallStableInfluences_of_log_bound {n : ℕ} (S : Finset (Fin n)) (ε δ : ℝ) (hε : 0 < ε) (hδ : δ ∈ Set.Ioc 0 1) (hcard : Real.log (Real.exp 1 / ε) / δ ≤ ↑S.card) : FABL.HasSmallStableInfluences ε δ (FABL.parityFunction S).toReal
The logarithmic support-size condition in Example 6.10 implies the required stable-influence bound for parity.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.exists_stableInfluence_ge_of_isKJunta_of_balanced {n : ℕ} (f : FABL.BooleanFunction n) (k : ℕ) (hk : 0 < k) (hjunta : FABL.IsKJunta f k) (hbalanced : FABL.IsBalanced f.toReal) (δ : ℝ) (hδ : δ ∈ Set.Ioo 0 1) : ∃ i, (1 - δ) ^ (k - 1) / ↑k ≤ FABL.stableInfluence (1 - δ) f.toReal i
theorem FABL.exists_stableInfluence_ge_of_isKJunta_of_balanced {n : ℕ} (f : FABL.BooleanFunction n) (k : ℕ) (hk : 0 < k) (hjunta : FABL.IsKJunta f k) (hbalanced : FABL.IsBalanced f.toReal) (δ : ℝ) (hδ : δ ∈ Set.Ioo 0 1) : ∃ i, (1 - δ) ^ (k - 1) / ↑k ≤ FABL.stableInfluence (1 - δ) f.toReal i
An unbiased Boolean `k`-junta has a coordinate with the stable-influence lower bound in Example 6.10.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.not_hasSmallStableInfluences_of_isKJunta_of_balanced {n : ℕ} (f : FABL.BooleanFunction n) (k : ℕ) (hk : 0 < k) (hjunta : FABL.IsKJunta f k) (hbalanced : FABL.IsBalanced f.toReal) (δ : ℝ) (hδ : δ ∈ Set.Ioo 0 1) : ¬FABL.HasSmallStableInfluences ((1 - δ) ^ k / ↑k) δ f.toReal
theorem FABL.not_hasSmallStableInfluences_of_isKJunta_of_balanced {n : ℕ} (f : FABL.BooleanFunction n) (k : ℕ) (hk : 0 < k) (hjunta : FABL.IsKJunta f k) (hbalanced : FABL.IsBalanced f.toReal) (δ : ℝ) (hδ : δ ∈ Set.Ioo 0 1) : ¬FABL.HasSmallStableInfluences ((1 - δ) ^ k / ↑k) δ f.toReal
Hence an unbiased Boolean `k`-junta fails the smaller threshold printed in Example 6.10.
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defdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
def FABL.leadingCoordinateTimes {n : ℕ} (g : FABL.BooleanFunction n) : FABL.BooleanFunction (n + 1)
def FABL.leadingCoordinateTimes {n : ℕ} (g : FABL.BooleanFunction n) : FABL.BooleanFunction (n + 1)
Adjoin a leading signed coordinate and multiply it by a Boolean function on the tail coordinates.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.one_sub_sqrt_le_stableInfluence_leadingCoordinateTimes_majority_odd (m : ℕ) (δ : ℝ) (hδ : δ ∈ Set.Icc 0 1) : 1 - √δ ≤ FABL.stableInfluence (1 - δ) (FABL.leadingCoordinateTimes (FABL.majority (2 * m + 1))).toReal 0
theorem FABL.one_sub_sqrt_le_stableInfluence_leadingCoordinateTimes_majority_odd (m : ℕ) (δ : ℝ) (hδ : δ ∈ Set.Icc 0 1) : 1 - √δ ≤ FABL.stableInfluence (1 - δ) (FABL.leadingCoordinateTimes (FABL.majority (2 * m + 1))).toReal 0
For odd majority, the leading-coordinate product has stable influence at least `1 - √δ` in the new coordinate.
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FABL.IsLowDegreeFourierRegular[complete] -
FABL.IsFourierRegular.isLowDegreeFourierRegular[complete] -
FABL.isLowDegreeFourierRegular_dimension_iff[complete]
Definition 6.11. A function f:\{-1,1\}^n\to\mathbb R is
(\epsilon,k)-regular if
|\widehat f(S)|\le\epsilon
\qquad\text{whenever }0<|S|\le k.
Equivalently, the degree-at-most-k part f^{\le k} is
\epsilon-regular. For k=n (or k=\infty) this is ordinary
\epsilon-regularity. An (\epsilon,k)-regular probability density, and
its associated distribution, is also called
(\epsilon,k)-wise independent.
Lean code for Definition6.1.11●3 declarations
Associated Lean declarations
-
FABL.IsLowDegreeFourierRegular[complete]
-
FABL.IsFourierRegular.isLowDegreeFourierRegular[complete]
-
FABL.isLowDegreeFourierRegular_dimension_iff[complete]
-
FABL.IsLowDegreeFourierRegular[complete] -
FABL.IsFourierRegular.isLowDegreeFourierRegular[complete] -
FABL.isLowDegreeFourierRegular_dimension_iff[complete]
-
defdefined in FABL/Chapter06/Pseudorandomness/Regularity.leancomplete
def FABL.IsLowDegreeFourierRegular {n : ℕ} (ε : ℝ) (k : ℕ) (f : FABL.SignCube n → ℝ) : Prop
def FABL.IsLowDegreeFourierRegular {n : ℕ} (ε : ℝ) (k : ℕ) (f : FABL.SignCube n → ℝ) : Prop
Low-degree Fourier regularity through level `k`.
-
theoremdefined in FABL/Chapter06/Pseudorandomness/Regularity.leancomplete
theorem FABL.IsFourierRegular.isLowDegreeFourierRegular {n : ℕ} {ε : ℝ} {f : FABL.SignCube n → ℝ} (h : FABL.IsFourierRegular ε f) (k : ℕ) : FABL.IsLowDegreeFourierRegular ε k f
theorem FABL.IsFourierRegular.isLowDegreeFourierRegular {n : ℕ} {ε : ℝ} {f : FABL.SignCube n → ℝ} (h : FABL.IsFourierRegular ε f) (k : ℕ) : FABL.IsLowDegreeFourierRegular ε k f
Ordinary Fourier regularity implies every low-degree version with the same parameter.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Regularity.leancomplete
theorem FABL.isLowDegreeFourierRegular_dimension_iff {n : ℕ} (ε : ℝ) (f : FABL.SignCube n → ℝ) : FABL.IsLowDegreeFourierRegular ε n f ↔ FABL.IsFourierRegular ε f
theorem FABL.isLowDegreeFourierRegular_dimension_iff {n : ℕ} (ε : ℝ) (f : FABL.SignCube n → ℝ) : FABL.IsLowDegreeFourierRegular ε n f ↔ FABL.IsFourierRegular ε f
In dimension `n`, regularity through level `n` is ordinary regularity.
Proposition 6.12. Let f:\{-1,1\}^n\to\mathbb R,
\epsilon\ge0, and k\in\mathbb N.
(1) If f is (\epsilon,k)-regular, then every restriction fixing at
most k coordinates changes the mean of f by at most 2^k\epsilon.
(2) If f is not (\epsilon,k)-regular, then some restriction fixing at
most k coordinates changes the mean of f by more than \epsilon.
Lean code for Proposition6.1.12●2 theorems
Associated Lean declarations
-
theoremdefined in FABL/Chapter06/Pseudorandomness/RegularityCharacterizations.leancomplete
theorem FABL.IsLowDegreeFourierRegular.abs_mean_signRestriction_sub_mean_le {n : ℕ} {ε : ℝ} {k : ℕ} {f : FABL.SignCube n → ℝ} (hregular : FABL.IsLowDegreeFourierRegular ε k f) (hε : 0 ≤ ε) (J : Finset (Fin n)) (z : FABL.FixedSignCube J) (hJ : Fintype.card (FABL.FixedIndex J) ≤ k) : |FABL.mean (FABL.signRestriction f J z) - FABL.mean f| ≤ 2 ^ k * ε
theorem FABL.IsLowDegreeFourierRegular.abs_mean_signRestriction_sub_mean_le {n : ℕ} {ε : ℝ} {k : ℕ} {f : FABL.SignCube n → ℝ} (hregular : FABL.IsLowDegreeFourierRegular ε k f) (hε : 0 ≤ ε) (J : Finset (Fin n)) (z : FABL.FixedSignCube J) (hJ : Fintype.card (FABL.FixedIndex J) ≤ k) : |FABL.mean (FABL.signRestriction f J z) - FABL.mean f| ≤ 2 ^ k * ε
O'Donnell, Proposition 6.12(1): regularity through level `k` controls the change in mean under every restriction fixing at most `k` coordinates.
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theoremdefined in FABL/Chapter06/Pseudorandomness/RegularityCharacterizations.leancomplete
theorem FABL.exists_signRestriction_mean_change_gt_of_not_isLowDegreeFourierRegular {n : ℕ} (f : FABL.SignCube n → ℝ) {ε : ℝ} {k : ℕ} (hregular : ¬FABL.IsLowDegreeFourierRegular ε k f) : ∃ J, Fintype.card (FABL.FixedIndex J) ≤ k ∧ ∃ z, ε < |FABL.mean (FABL.signRestriction f J z) - FABL.mean f|
theorem FABL.exists_signRestriction_mean_change_gt_of_not_isLowDegreeFourierRegular {n : ℕ} (f : FABL.SignCube n → ℝ) {ε : ℝ} {k : ℕ} (hregular : ¬FABL.IsLowDegreeFourierRegular ε k f) : ∃ J, Fintype.card (FABL.FixedIndex J) ≤ k ∧ ∃ z, ε < |FABL.mean (FABL.signRestriction f J z) - FABL.mean f|
O'Donnell, Proposition 6.12(2): failure of regularity through level `k` is witnessed by a restriction fixing at most `k` coordinates whose mean changes by more than `ε`.
Proposition 6.13. Let f:\{-1,1\}^n\to\mathbb R,
\epsilon\ge0, and k\in\mathbb N.
(1) If f is (\epsilon,k)-regular, then
\operatorname{Cov}[f,h]
\le\lVert\widehat h\rVert_1\epsilon
for every h:\{-1,1\}^n\to\mathbb R of degree at most k. In particular,
\operatorname{Cov}[f,h]\le 2^{k/2}\epsilon
for every Boolean-valued k-junta h.
(2) If f is not (\epsilon,k)-regular, then there is a Boolean-valued
k-junta h such that
\operatorname{Cov}[f,h]>\epsilon.
Lean code for Proposition6.1.13●3 theorems
Associated Lean declarations
-
theoremdefined in FABL/Chapter06/Pseudorandomness/RegularityCharacterizations.leancomplete
theorem FABL.IsLowDegreeFourierRegular.covariance_le_fourierOneNorm_mul {n : ℕ} {ε : ℝ} {k : ℕ} {f h : FABL.SignCube n → ℝ} (hregular : FABL.IsLowDegreeFourierRegular ε k f) (hdegree : FABL.fourierDegree h ≤ k) (hε : 0 ≤ ε) : FABL.covariance f h ≤ FABL.fourierOneNorm h * ε
theorem FABL.IsLowDegreeFourierRegular.covariance_le_fourierOneNorm_mul {n : ℕ} {ε : ℝ} {k : ℕ} {f h : FABL.SignCube n → ℝ} (hregular : FABL.IsLowDegreeFourierRegular ε k f) (hdegree : FABL.fourierDegree h ≤ k) (hε : 0 ≤ ε) : FABL.covariance f h ≤ FABL.fourierOneNorm h * ε
O'Donnell, Proposition 6.13(1): regularity through level `k` bounds covariance with every degree-at-most-`k` function by its Fourier `1`-norm times `ε`.
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theoremdefined in FABL/Chapter06/Pseudorandomness/RegularityCharacterizations.leancomplete
theorem FABL.IsLowDegreeFourierRegular.covariance_booleanJunta_le {n : ℕ} {ε : ℝ} {k : ℕ} {f : FABL.SignCube n → ℝ} (hregular : FABL.IsLowDegreeFourierRegular ε k f) (hε : 0 ≤ ε) (h : FABL.BooleanFunction n) (hjunta : FABL.IsKJunta h k) : FABL.covariance f h.toReal ≤ 2 ^ (↑k / 2) * ε
theorem FABL.IsLowDegreeFourierRegular.covariance_booleanJunta_le {n : ℕ} {ε : ℝ} {k : ℕ} {f : FABL.SignCube n → ℝ} (hregular : FABL.IsLowDegreeFourierRegular ε k f) (hε : 0 ≤ ε) (h : FABL.BooleanFunction n) (hjunta : FABL.IsKJunta h k) : FABL.covariance f h.toReal ≤ 2 ^ (↑k / 2) * ε
The Boolean-junta specialization of Proposition 6.13(1).
-
theoremdefined in FABL/Chapter06/Pseudorandomness/RegularityCharacterizations.leancomplete
theorem FABL.exists_booleanJunta_covariance_gt_of_not_isLowDegreeFourierRegular {n : ℕ} (f : FABL.SignCube n → ℝ) {ε : ℝ} {k : ℕ} (hregular : ¬FABL.IsLowDegreeFourierRegular ε k f) : ∃ h, FABL.IsKJunta h k ∧ ε < FABL.covariance f h.toReal
theorem FABL.exists_booleanJunta_covariance_gt_of_not_isLowDegreeFourierRegular {n : ℕ} (f : FABL.SignCube n → ℝ) {ε : ℝ} {k : ℕ} (hregular : ¬FABL.IsLowDegreeFourierRegular ε k f) : ∃ h, FABL.IsKJunta h k ∧ ε < FABL.covariance f h.toReal
O'Donnell, Proposition 6.13(2): failure of regularity through level `k` is witnessed by positive covariance with a Boolean `k`-junta.
Corollary 6.14. For f:\{-1,1\}^n\to\mathbb R, the following are
equivalent:
(1) f is (0,k)-regular.
(2) Every restriction of at most k coordinates leaves \mathbb E[f]
unchanged.
(3) \operatorname{Cov}[f,h]=0 for every Boolean-valued k-junta h.
If f is a probability density, condition (3) is equivalent to
\mathbb E_{\boldsymbol x\sim f}[h(\boldsymbol x)]=\mathbb E[h]
for every Boolean-valued k-junta h.
Lean code for Corollary6.1.14●3 theorems
Associated Lean declarations
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theoremdefined in FABL/Chapter06/Pseudorandomness/RegularityCharacterizations.leancomplete
theorem FABL.isLowDegreeFourierRegular_zero_iff_forall_mean_signRestriction_eq {n : ℕ} (f : FABL.SignCube n → ℝ) (k : ℕ) : FABL.IsLowDegreeFourierRegular 0 k f ↔ ∀ (J : Finset (Fin n)) (z : FABL.FixedSignCube J), Fintype.card (FABL.FixedIndex J) ≤ k → FABL.mean (FABL.signRestriction f J z) = FABL.mean f
theorem FABL.isLowDegreeFourierRegular_zero_iff_forall_mean_signRestriction_eq {n : ℕ} (f : FABL.SignCube n → ℝ) (k : ℕ) : FABL.IsLowDegreeFourierRegular 0 k f ↔ ∀ (J : Finset (Fin n)) (z : FABL.FixedSignCube J), Fintype.card (FABL.FixedIndex J) ≤ k → FABL.mean (FABL.signRestriction f J z) = FABL.mean f
O'Donnell, Corollary 6.14: zero regularity through level `k` is equivalent to invariance of the mean under every restriction fixing at most `k` coordinates.
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theoremdefined in FABL/Chapter06/Pseudorandomness/RegularityCharacterizations.leancomplete
theorem FABL.isLowDegreeFourierRegular_zero_iff_forall_covariance_booleanJunta_eq_zero {n : ℕ} (f : FABL.SignCube n → ℝ) (k : ℕ) : FABL.IsLowDegreeFourierRegular 0 k f ↔ ∀ (h : FABL.BooleanFunction n), FABL.IsKJunta h k → FABL.covariance f h.toReal = 0
theorem FABL.isLowDegreeFourierRegular_zero_iff_forall_covariance_booleanJunta_eq_zero {n : ℕ} (f : FABL.SignCube n → ℝ) (k : ℕ) : FABL.IsLowDegreeFourierRegular 0 k f ↔ ∀ (h : FABL.BooleanFunction n), FABL.IsKJunta h k → FABL.covariance f h.toReal = 0
O'Donnell, Corollary 6.14: zero regularity through level `k` is equivalent to vanishing covariance against every Boolean `k`-junta.
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theoremdefined in FABL/Chapter06/Pseudorandomness/RegularityCharacterizations.leancomplete
theorem FABL.ProbabilityDensity.isLowDegreeFourierRegular_zero_iff_forall_expectation_booleanJunta_eq_mean {n : ℕ} (φ : FABL.ProbabilityDensity n) (k : ℕ) : FABL.IsLowDegreeFourierRegular 0 k (FABL.binaryFunctionOnSignCube φ.toFun) ↔ ∀ (h : FABL.BooleanFunction n), FABL.IsKJunta h k → (φ.expectation fun x => h.toReal ((FABL.binaryCubeSignEquiv n) x)) = FABL.mean h.toReal
theorem FABL.ProbabilityDensity.isLowDegreeFourierRegular_zero_iff_forall_expectation_booleanJunta_eq_mean {n : ℕ} (φ : FABL.ProbabilityDensity n) (k : ℕ) : FABL.IsLowDegreeFourierRegular 0 k (FABL.binaryFunctionOnSignCube φ.toFun) ↔ ∀ (h : FABL.BooleanFunction n), FABL.IsKJunta h k → (φ.expectation fun x => h.toReal ((FABL.binaryCubeSignEquiv n) x)) = FABL.mean h.toReal
The probability-density form of Corollary 6.14: zero low-degree regularity is equivalent to matching uniform expectation on every Boolean `k`-junta.
Definition 6.15. A Boolean-valued (0,k)-regular function is
kth-order correlation immune. If it is also unbiased, it is
k-resilient. A (0,k)-regular probability density, and its associated
distribution, is k-wise independent.
Lean code for Definition6.1.15●2 definitions
Associated Lean declarations
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FABL.IsCorrelationImmune[complete]
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FABL.IsResilient[complete]
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FABL.IsCorrelationImmune[complete] -
FABL.IsResilient[complete]
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defdefined in FABL/Chapter06/Pseudorandomness/CorrelationImmunity.leancomplete
def FABL.IsCorrelationImmune {n : ℕ} (k : ℕ) (f : FABL.BooleanFunction n) : Prop
def FABL.IsCorrelationImmune {n : ℕ} (k : ℕ) (f : FABL.BooleanFunction n) : Prop
A Boolean function is correlation immune of order `k` when its nonconstant Fourier coefficients through level `k` vanish.
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defdefined in FABL/Chapter06/Pseudorandomness/CorrelationImmunity.leancomplete
def FABL.IsResilient {n : ℕ} (k : ℕ) (f : FABL.BooleanFunction n) : Prop
def FABL.IsResilient {n : ℕ} (k : ℕ) (f : FABL.BooleanFunction n) : Prop
A correlation-immune Boolean function is resilient when it is also unbiased.
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FABL.parityFunction_isResilient[complete] -
FABL.parityTimes[complete] -
FABL.fourierCoeff_parityTimes[complete] -
FABL.parityTimes_isResilient_of_dependsOn_compl[complete] -
FABL.firstTwoThirds[complete] -
FABL.lastTwoThirds[complete] -
FABL.card_firstTwoThirds_symmDiff_lastTwoThirds[complete] -
FABL.correlationImmuneAndExample[complete] -
FABL.fourierCoeff_correlationImmuneAndExample[complete] -
FABL.correlationImmuneAndExample_isCorrelationImmune[complete] -
FABL.mean_correlationImmuneAndExample[complete] -
FABL.correlationImmuneAndExample_not_isResilient[complete] -
FABL.uniformProbability_correlationImmuneAndExample_eq_true[complete]
Example 6.16. If |S|=k+1, then \chi_S is k-resilient; more
generally, so is \chi_Sg whenever g does not depend on any coordinate
in S. For a correlation-immune function that is not resilient, let
h:\{-1,1\}^{3m}\to\{-1,1\} be
h=\chi_{\{1,\ldots,2m\}}\wedge\chi_{\{m+1,\ldots,3m\}}.
It is True on one quarter of its inputs, but fixing fewer than 2m input
bits does not change this bias. Thus h is correlation immune of order
2m-1 and is not unbiased.
Lean code for Lemma6.1.16●13 declarations
Associated Lean declarations
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FABL.parityFunction_isResilient[complete]
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FABL.parityTimes[complete]
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FABL.fourierCoeff_parityTimes[complete]
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FABL.parityTimes_isResilient_of_dependsOn_compl[complete]
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FABL.firstTwoThirds[complete]
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FABL.lastTwoThirds[complete]
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FABL.card_firstTwoThirds_symmDiff_lastTwoThirds[complete]
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FABL.correlationImmuneAndExample[complete]
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FABL.fourierCoeff_correlationImmuneAndExample[complete]
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FABL.correlationImmuneAndExample_isCorrelationImmune[complete]
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FABL.mean_correlationImmuneAndExample[complete]
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FABL.correlationImmuneAndExample_not_isResilient[complete]
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FABL.uniformProbability_correlationImmuneAndExample_eq_true[complete]
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FABL.parityFunction_isResilient[complete] -
FABL.parityTimes[complete] -
FABL.fourierCoeff_parityTimes[complete] -
FABL.parityTimes_isResilient_of_dependsOn_compl[complete] -
FABL.firstTwoThirds[complete] -
FABL.lastTwoThirds[complete] -
FABL.card_firstTwoThirds_symmDiff_lastTwoThirds[complete] -
FABL.correlationImmuneAndExample[complete] -
FABL.fourierCoeff_correlationImmuneAndExample[complete] -
FABL.correlationImmuneAndExample_isCorrelationImmune[complete] -
FABL.mean_correlationImmuneAndExample[complete] -
FABL.correlationImmuneAndExample_not_isResilient[complete] -
FABL.uniformProbability_correlationImmuneAndExample_eq_true[complete]
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theoremdefined in FABL/Chapter06/Pseudorandomness/CorrelationImmunity.leancomplete
theorem FABL.parityFunction_isResilient {n : ℕ} (S : Finset (Fin n)) (k : ℕ) (hcard : S.card = k + 1) : FABL.IsResilient k (FABL.parityFunction S)
theorem FABL.parityFunction_isResilient {n : ℕ} (S : Finset (Fin n)) (k : ℕ) (hcard : S.card = k + 1) : FABL.IsResilient k (FABL.parityFunction S)
A parity on `k + 1` coordinates is `k`-resilient.
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defdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
def FABL.parityTimes {n : ℕ} (S : Finset (Fin n)) (g : FABL.BooleanFunction n) : FABL.BooleanFunction n
def FABL.parityTimes {n : ℕ} (S : Finset (Fin n)) (g : FABL.BooleanFunction n) : FABL.BooleanFunction n
Multiply a parity by a Boolean function on the same cube.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.fourierCoeff_parityTimes {n : ℕ} (S T : Finset (Fin n)) (g : FABL.BooleanFunction n) : FABL.fourierCoeff (FABL.parityTimes S g).toReal T = FABL.fourierCoeff g.toReal (symmDiff S T)
theorem FABL.fourierCoeff_parityTimes {n : ℕ} (S T : Finset (Fin n)) (g : FABL.BooleanFunction n) : FABL.fourierCoeff (FABL.parityTimes S g).toReal T = FABL.fourierCoeff g.toReal (symmDiff S T)
Multiplication by a parity translates the Fourier spectrum by symmetric difference.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.parityTimes_isResilient_of_dependsOn_compl {n : ℕ} (S : Finset (Fin n)) (g : FABL.BooleanFunction n) (k : ℕ) (hcard : S.card = k + 1) (hdepends : DependsOn g ↑(Finset.univ \ S)) : FABL.IsResilient k (FABL.parityTimes S g)
theorem FABL.parityTimes_isResilient_of_dependsOn_compl {n : ℕ} (S : Finset (Fin n)) (g : FABL.BooleanFunction n) (k : ℕ) (hcard : S.card = k + 1) (hdepends : DependsOn g ↑(Finset.univ \ S)) : FABL.IsResilient k (FABL.parityTimes S g)
A parity on `k+1` coordinates times a function independent of those coordinates is `k`-resilient.
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defdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
def FABL.firstTwoThirds (m : ℕ) (hm : 0 < m) : Finset (Fin (3 * m))
def FABL.firstTwoThirds (m : ℕ) (hm : 0 < m) : Finset (Fin (3 * m))
The first two thirds of the coordinates in the explicit `3m`-variable construction.
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defdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
def FABL.lastTwoThirds (m : ℕ) (hm : 0 < m) : Finset (Fin (3 * m))
def FABL.lastTwoThirds (m : ℕ) (hm : 0 < m) : Finset (Fin (3 * m))
The last two thirds of the coordinates in the explicit `3m`-variable construction.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.card_firstTwoThirds_symmDiff_lastTwoThirds (m : ℕ) (hm : 0 < m) : (symmDiff (FABL.firstTwoThirds m hm) (FABL.lastTwoThirds m hm)).card = 2 * m
theorem FABL.card_firstTwoThirds_symmDiff_lastTwoThirds (m : ℕ) (hm : 0 < m) : (symmDiff (FABL.firstTwoThirds m hm) (FABL.lastTwoThirds m hm)).card = 2 * m
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defdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
def FABL.correlationImmuneAndExample (m : ℕ) (hm : 0 < m) : FABL.BooleanFunction (3 * m)
def FABL.correlationImmuneAndExample (m : ℕ) (hm : 0 < m) : FABL.BooleanFunction (3 * m)
The correlation-immune but biased function from Example 6.16.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.fourierCoeff_correlationImmuneAndExample (m : ℕ) (hm : 0 < m) (T : Finset (Fin (3 * m))) : FABL.fourierCoeff (FABL.correlationImmuneAndExample m hm).toReal T = ((((if T = ∅ then 1 else 0) + if FABL.firstTwoThirds m hm = T then 1 else 0) + if FABL.lastTwoThirds m hm = T then 1 else 0) - if symmDiff (FABL.firstTwoThirds m hm) (FABL.lastTwoThirds m hm) = T then 1 else 0) / 2
theorem FABL.fourierCoeff_correlationImmuneAndExample (m : ℕ) (hm : 0 < m) (T : Finset (Fin (3 * m))) : FABL.fourierCoeff (FABL.correlationImmuneAndExample m hm).toReal T = ((((if T = ∅ then 1 else 0) + if FABL.firstTwoThirds m hm = T then 1 else 0) + if FABL.lastTwoThirds m hm = T then 1 else 0) - if symmDiff (FABL.firstTwoThirds m hm) (FABL.lastTwoThirds m hm) = T then 1 else 0) / 2
The exact Fourier coefficients of the explicit construction.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.correlationImmuneAndExample_isCorrelationImmune (m : ℕ) (hm : 0 < m) : FABL.IsCorrelationImmune (2 * m - 1) (FABL.correlationImmuneAndExample m hm)
theorem FABL.correlationImmuneAndExample_isCorrelationImmune (m : ℕ) (hm : 0 < m) : FABL.IsCorrelationImmune (2 * m - 1) (FABL.correlationImmuneAndExample m hm)
The explicit function is correlation immune of order `2m-1`.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.mean_correlationImmuneAndExample (m : ℕ) (hm : 0 < m) : FABL.mean (FABL.correlationImmuneAndExample m hm).toReal = 1 / 2
theorem FABL.mean_correlationImmuneAndExample (m : ℕ) (hm : 0 < m) : FABL.mean (FABL.correlationImmuneAndExample m hm).toReal = 1 / 2
The construction has mean `1/2`, so it is not resilient.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.correlationImmuneAndExample_not_isResilient (m : ℕ) (hm : 0 < m) : ¬FABL.IsResilient (2 * m - 1) (FABL.correlationImmuneAndExample m hm)
theorem FABL.correlationImmuneAndExample_not_isResilient (m : ℕ) (hm : 0 < m) : ¬FABL.IsResilient (2 * m - 1) (FABL.correlationImmuneAndExample m hm)
The explicit correlation-immune construction is not resilient.
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theoremdefined in FABL/Chapter06/Pseudorandomness/Examples.leancomplete
theorem FABL.uniformProbability_correlationImmuneAndExample_eq_true (m : ℕ) (hm : 0 < m) : (FABL.uniformProbability fun x => FABL.correlationImmuneAndExample m hm x = -1) = 1 / 4
theorem FABL.uniformProbability_correlationImmuneAndExample_eq_true (m : ℕ) (hm : 0 < m) : (FABL.uniformProbability fun x => FABL.correlationImmuneAndExample m hm x = -1) = 1 / 4
In the book's `-1 = True` convention, the construction is True on exactly one quarter of the cube.
Exercise 6.2. Prove Fact 6.8.
Lean code for Lemma6.1.17●3 theorems
Associated Lean declarations
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theoremdefined in FABL/Chapter06/Pseudorandomness/RandomFunctions.leancomplete
theorem FABL.expect_sq_fourierCoeff_uniformBooleanFunction {n : ℕ} (S : Finset (Fin n)) : (Finset.univ.expect fun f => FABL.fourierCoeff f.toReal S ^ 2) = 1 / 2 ^ n
theorem FABL.expect_sq_fourierCoeff_uniformBooleanFunction {n : ℕ} (S : Finset (Fin n)) : (Finset.univ.expect fun f => FABL.fourierCoeff f.toReal S ^ 2) = 1 / 2 ^ n
Every squared Fourier coefficient has mean `2⁻ⁿ` for a uniformly random sign function.
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theoremdefined in FABL/Chapter06/Pseudorandomness/RandomFunctions.leancomplete
theorem FABL.sum_pow_card_sub_one_filter_mem {n : ℕ} (ρ : ℝ) (i : Fin n) : ∑ S with i ∈ S, ρ ^ (S.card - 1) = (1 + ρ) ^ (n - 1)
theorem FABL.sum_pow_card_sub_one_filter_mem {n : ℕ} (ρ : ℝ) (i : Fin n) : ∑ S with i ∈ S, ρ ^ (S.card - 1) = (1 + ρ) ^ (n - 1)
The subset weights in one stable influence sum to `(1 + ρ)ⁿ⁻¹`.
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theoremdefined in FABL/Chapter06/Pseudorandomness/RandomFunctions.leancomplete
theorem FABL.pmfExpectation_uniformBooleanFunction_stableInfluence {n : ℕ} (ρ : ℝ) (i : Fin n) : (FABL.pmfExpectation (FABL.uniformPMF (FABL.BooleanFunction n)) fun f => FABL.stableInfluence ρ f.toReal i) = (1 + ρ) ^ (n - 1) / 2 ^ n
theorem FABL.pmfExpectation_uniformBooleanFunction_stableInfluence {n : ℕ} (ρ : ℝ) (i : Fin n) : (FABL.pmfExpectation (FABL.uniformPMF (FABL.BooleanFunction n)) fun f => FABL.stableInfluence ρ f.toReal i) = (1 + ρ) ^ (n - 1) / 2 ^ n
Exercise 6.2: before substituting `ρ = 1 - δ`, the expected stable influence of a uniformly random sign function is `(1 + ρ)ⁿ⁻¹ / 2ⁿ`.
Exercise 6.8. Prove Proposition 6.13.
Lean code for Lemma6.1.18●4 theorems
Associated Lean declarations
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theoremdefined in FABL/Chapter06/Pseudorandomness/RegularityCharacterizations.leancomplete
theorem FABL.fourierCoeff_eq_zero_of_dependsOn_of_not_subset {n : ℕ} (f : FABL.SignCube n → ℝ) {J T : Finset (Fin n)} (hdepends : DependsOn f ↑J) (hT : ¬T ⊆ J) : FABL.fourierCoeff f T = 0
theorem FABL.fourierCoeff_eq_zero_of_dependsOn_of_not_subset {n : ℕ} (f : FABL.SignCube n → ℝ) {J T : Finset (Fin n)} (hdepends : DependsOn f ↑J) (hT : ¬T ⊆ J) : FABL.fourierCoeff f T = 0
A function depending only on `J` has no Fourier coefficient supported outside `J`.
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theoremdefined in FABL/Chapter06/Pseudorandomness/RegularityCharacterizations.leancomplete
theorem FABL.fourierDegree_toReal_le_of_isKJunta {n : ℕ} (h : FABL.BooleanFunction n) {k : ℕ} (hjunta : FABL.IsKJunta h k) : FABL.fourierDegree h.toReal ≤ k
theorem FABL.fourierDegree_toReal_le_of_isKJunta {n : ℕ} (h : FABL.BooleanFunction n) {k : ℕ} (hjunta : FABL.IsKJunta h k) : FABL.fourierDegree h.toReal ≤ k
A Boolean `k`-junta has Fourier degree at most `k`.
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theoremdefined in FABL/Chapter06/Pseudorandomness/RegularityCharacterizations.leancomplete
theorem FABL.fourierOneNorm_toReal_le_two_rpow_half_of_isKJunta {n : ℕ} (h : FABL.BooleanFunction n) {k : ℕ} (hjunta : FABL.IsKJunta h k) : FABL.fourierOneNorm h.toReal ≤ 2 ^ (↑k / 2)
theorem FABL.fourierOneNorm_toReal_le_two_rpow_half_of_isKJunta {n : ℕ} (h : FABL.BooleanFunction n) {k : ℕ} (hjunta : FABL.IsKJunta h k) : FABL.fourierOneNorm h.toReal ≤ 2 ^ (↑k / 2)
The Fourier `1`-norm of a Boolean `k`-junta is at most `2^(k/2)`.
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theoremdefined in FABL/Chapter06/Pseudorandomness/RegularityCharacterizations.leancomplete
theorem FABL.IsLowDegreeFourierRegular.abs_covariance_le_fourierOneNorm_mul {n : ℕ} {ε : ℝ} {k : ℕ} {f h : FABL.SignCube n → ℝ} (hregular : FABL.IsLowDegreeFourierRegular ε k f) (hdegree : FABL.fourierDegree h ≤ k) (hε : 0 ≤ ε) : |FABL.covariance f h| ≤ FABL.fourierOneNorm h * ε
theorem FABL.IsLowDegreeFourierRegular.abs_covariance_le_fourierOneNorm_mul {n : ℕ} {ε : ℝ} {k : ℕ} {f h : FABL.SignCube n → ℝ} (hregular : FABL.IsLowDegreeFourierRegular ε k f) (hdegree : FABL.fourierDegree h ≤ k) (hε : 0 ≤ ε) : |FABL.covariance f h| ≤ FABL.fourierOneNorm h * ε
The absolute covariance bound underlying Proposition 6.13(1).