Analysis of Boolean Functions in Lean

2.2. Influences and derivatives🔗

Definition2.2.1
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L∃∀N

Definition 2.12. A coordinate i\in[n] is pivotal for f:\{-1,1\}^n\to\{-1,1\} on input x if f(x)\ne f(x^{\oplus i}), where x^{\oplus i}=(x_1,\ldots,x_{i-1},-x_i,x_{i+1},\ldots,x_n).

Lean code for Definition2.2.14 declarations
  • defdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    def FABL.setCoordinate {n : } (x : FABL.SignCube n) (i : Fin n)
      (b : FABL.Sign) : FABL.SignCube n
    def FABL.setCoordinate {n : }
      (x : FABL.SignCube n) (i : Fin n)
      (b : FABL.Sign) : FABL.SignCube n
    Set coordinate `i` of a sign-cube input to `b`, using Mathlib's `Function.update`. 
  • defdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    def FABL.flipCoordinate {n : } (x : FABL.SignCube n) (i : Fin n) :
      FABL.SignCube n
    def FABL.flipCoordinate {n : }
      (x : FABL.SignCube n) (i : Fin n) :
      FABL.SignCube n
    O'Donnell, Definition 2.12: flip coordinate `i` of a sign-cube input. 
  • defdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    def FABL.IsPivotal.{u_1} {n : } {β : Type u_1} (f : FABL.SignCube n  β)
      (i : Fin n) (x : FABL.SignCube n) : Prop
    def FABL.IsPivotal.{u_1} {n : }
      {β : Type u_1} (f : FABL.SignCube n  β)
      (i : Fin n) (x : FABL.SignCube n) : Prop
    O'Donnell, Definition 2.12: coordinate `i` is pivotal for `f` at `x`. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    theorem FABL.isPivotal_iff_setCoordinate_ne.{u_1} {n : } {β : Type u_1}
      (f : FABL.SignCube n  β) (i : Fin n) (x : FABL.SignCube n) :
      FABL.IsPivotal f i x 
        f (FABL.setCoordinate x i 1)  f (FABL.setCoordinate x i (-1))
    theorem FABL.isPivotal_iff_setCoordinate_ne.{u_1}
      {n : } {β : Type u_1}
      (f : FABL.SignCube n  β) (i : Fin n)
      (x : FABL.SignCube n) :
      FABL.IsPivotal f i x 
        f (FABL.setCoordinate x i 1) 
          f (FABL.setCoordinate x i (-1))
    Pivotality is equivalently disagreement between the two restrictions of one coordinate. 
Definition2.2.2
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Lemma 2.2.3
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L∃∀N

Definition 2.13. The influence of coordinate i on f:\{-1,1\}^n\to\{-1,1\} is the probability that i is pivotal for a uniformly random input: \operatorname{Inf}_i[f] =\Pr_{\boldsymbol{x}\sim\{-1,1\}^n} [f(\boldsymbol{x})\ne f(\boldsymbol{x}^{\oplus i})].

Lean code for Definition2.2.21 definition
  • defdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    def FABL.booleanInfluence {n : } (f : FABL.BooleanFunction n) (i : Fin n) :
      
    def FABL.booleanInfluence {n : }
      (f : FABL.BooleanFunction n)
      (i : Fin n) : 
    O'Donnell, Definition 2.13: pivotal probability for a Boolean-valued function. 
Lemma2.2.3
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Definition 2.2.1
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Lemma 2.2.4
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L∃∀N

Fact 2.14. For f:\{-1,1\}^n\to\{-1,1\}, \operatorname{Inf}_i[f] equals the fraction of dimension-i edges of the Hamming cube that are boundary edges. An edge (x,y) has dimension i when y=x^{\oplus i}, and it is a boundary edge when f(x)\ne f(y).

Lean code for Lemma2.2.34 declarations
  • abbrevdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    abbrev FABL.DimensionEdge {n : } (i : Fin n) : Type
    abbrev FABL.DimensionEdge {n : } (i : Fin n) :
      Type
    The canonical endpoint model for an undirected dimension-`i` edge: the endpoint whose
    `i`th coordinate is `+1`. 
  • defdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    def FABL.IsBoundaryDimensionEdge {n : } (f : FABL.BooleanFunction n)
      (i : Fin n) (e : FABL.DimensionEdge i) : Prop
    def FABL.IsBoundaryDimensionEdge {n : }
      (f : FABL.BooleanFunction n) (i : Fin n)
      (e : FABL.DimensionEdge i) : Prop
    Boundary status of the undirected dimension-`i` edge represented by its canonical
    endpoint. 
  • defdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    def FABL.dimensionEdgeBoundaryFraction {n : } (f : FABL.BooleanFunction n)
      (i : Fin n) : 
    def FABL.dimensionEdgeBoundaryFraction {n : }
      (f : FABL.BooleanFunction n)
      (i : Fin n) : 
    The fraction of undirected dimension-`i` edges crossing the boundary of a Boolean
    function. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    theorem FABL.booleanInfluence_eq_dimensionEdgeBoundaryFraction {n : }
      (f : FABL.BooleanFunction n) (i : Fin n) :
      FABL.booleanInfluence f i = FABL.dimensionEdgeBoundaryFraction f i
    theorem FABL.booleanInfluence_eq_dimensionEdgeBoundaryFraction
      {n : } (f : FABL.BooleanFunction n)
      (i : Fin n) :
      FABL.booleanInfluence f i =
        FABL.dimensionEdgeBoundaryFraction f i
    O'Donnell, Fact 2.14: Boolean influence is exactly the fraction of undirected
    dimension-`i` edges which are boundary edges. 
Lemma2.2.4

Example 2.15. For the dictator \chi_i, coordinate i is pivotal on every input, so \operatorname{Inf}_i[\chi_i]=1; for j\ne i, \operatorname{Inf}_j[\chi_i]=0. The same holds for the corresponding negated dictator. Every coordinate of a constant function has influence 0, and for every i\in[n], \operatorname{Inf}_i[\operatorname{OR}_n]=\operatorname{Inf}_i[\operatorname{AND}_n]=2^{1-n}. For \operatorname{Maj}_3, each influence is 1/2. More generally, for odd n, \operatorname{Inf}_i[\operatorname{Maj}_n] =\Pr[\text{exactly half of }n-1\text{ independent random bits are }1] =\binom{n-1}{(n-1)/2}2^{1-n} =\sqrt{\frac{2}{\pi n}}+O(n^{-3/2}).

Lean code for Lemma2.2.48 theorems
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    theorem FABL.booleanInfluence_dictator_self {n : } (i : Fin n) :
      FABL.booleanInfluence (FABL.dictator i) i = 1
    theorem FABL.booleanInfluence_dictator_self
      {n : } (i : Fin n) :
      FABL.booleanInfluence (FABL.dictator i)
          i =
        1
    O'Donnell, Example 2.15: the dictated coordinate has influence one. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    theorem FABL.booleanInfluence_dictator_of_ne {n : } (i j : Fin n)
      (hji : j  i) : FABL.booleanInfluence (FABL.dictator i) j = 0
    theorem FABL.booleanInfluence_dictator_of_ne
      {n : } (i j : Fin n) (hji : j  i) :
      FABL.booleanInfluence (FABL.dictator i)
          j =
        0
    O'Donnell, Example 2.15: every other coordinate has influence zero on a dictator. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    theorem FABL.booleanInfluence_neg_dictator {n : } (i j : Fin n) :
      FABL.booleanInfluence (fun x => -FABL.dictator i x) j =
        if j = i then 1 else 0
    theorem FABL.booleanInfluence_neg_dictator {n : }
      (i j : Fin n) :
      FABL.booleanInfluence
          (fun x => -FABL.dictator i x) j =
        if j = i then 1 else 0
    O'Donnell, Example 2.15: a negated dictator has the same exact influence profile as a
    dictator. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    theorem FABL.booleanInfluence_const {n : } (c : FABL.Sign) (i : Fin n) :
      FABL.booleanInfluence (fun x => c) i = 0
    theorem FABL.booleanInfluence_const {n : }
      (c : FABL.Sign) (i : Fin n) :
      FABL.booleanInfluence (fun x => c) i = 0
    O'Donnell, Example 2.15: every coordinate influence of a constant function is zero. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    theorem FABL.booleanInfluence_orFunction {n : } (i : Fin n) :
      FABL.booleanInfluence (FABL.orFunction n) i = 1 / 2 ^ (n - 1)
    theorem FABL.booleanInfluence_orFunction {n : }
      (i : Fin n) :
      FABL.booleanInfluence
          (FABL.orFunction n) i =
        1 / 2 ^ (n - 1)
    O'Donnell, Example 2.15: every coordinate of `ORₙ` has influence `2^(1-n)`. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    theorem FABL.booleanInfluence_andFunction {n : } (i : Fin n) :
      FABL.booleanInfluence (FABL.andFunction n) i = 1 / 2 ^ (n - 1)
    theorem FABL.booleanInfluence_andFunction {n : }
      (i : Fin n) :
      FABL.booleanInfluence
          (FABL.andFunction n) i =
        1 / 2 ^ (n - 1)
    O'Donnell, Example 2.15: every coordinate of `ANDₙ` has influence `2^(1-n)`. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    theorem FABL.booleanInfluence_majority_three (i : Fin 3) :
      FABL.booleanInfluence (FABL.majority 3) i = 1 / 2
    theorem FABL.booleanInfluence_majority_three
      (i : Fin 3) :
      FABL.booleanInfluence (FABL.majority 3)
          i =
        1 / 2
    O'Donnell, Example 2.15: every coordinate of three-bit majority has influence `1/2`. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/BooleanInfluence.lean
    complete
    theorem FABL.booleanInfluence_majority_odd (m : ) (i : Fin (2 * m + 1)) :
      FABL.booleanInfluence (FABL.majority (2 * m + 1)) i =
        ((2 * m).choose m) / 2 ^ (2 * m)
    theorem FABL.booleanInfluence_majority_odd (m : )
      (i : Fin (2 * m + 1)) :
      FABL.booleanInfluence
          (FABL.majority (2 * m + 1)) i =
        ((2 * m).choose m) / 2 ^ (2 * m)
    O'Donnell, Example 2.15 and Exercise 2.22(a): the exact influence of every coordinate
    of odd-arity majority. 

Definition 2.16. The ith discrete derivative operator sends f:\{-1,1\}^n\to\mathbb R to the function D_i f(x) =\frac{f(x^{(i\mapsto1)})-f(x^{(i\mapsto-1)})}{2}, where x^{(i\mapsto b)}=(x_1,\ldots,x_{i-1},b,x_{i+1},\ldots,x_n). The function D_i f does not depend on x_i, and D_i is linear: D_i(f+g)=D_i f+D_i g.

Lean code for Definition2.2.54 declarations
  • defdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    def FABL.discreteDerivative {n : } (i : Fin n) :
      (FABL.SignCube n  ) →ₗ[] FABL.SignCube n  
    def FABL.discreteDerivative {n : }
      (i : Fin n) :
      (FABL.SignCube n  ) →ₗ[]
        FABL.SignCube n  
    O'Donnell, Definition 2.16: the `i`th discrete derivative, as an `ℝ`-linear map. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.discreteDerivative_apply {n : } (i : Fin n)
      (f : FABL.SignCube n  ) (x : FABL.SignCube n) :
      (FABL.discreteDerivative i) f x =
        (f (FABL.setCoordinate x i 1) - f (FABL.setCoordinate x i (-1))) / 2
    theorem FABL.discreteDerivative_apply {n : }
      (i : Fin n) (f : FABL.SignCube n  )
      (x : FABL.SignCube n) :
      (FABL.discreteDerivative i) f x =
        (f (FABL.setCoordinate x i 1) -
            f (FABL.setCoordinate x i (-1))) /
          2
    The pointwise formula defining O'Donnell's discrete derivative. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.discreteDerivative_add {n : } (i : Fin n)
      (f g : FABL.SignCube n  ) :
      (FABL.discreteDerivative i) (f + g) =
        (FABL.discreteDerivative i) f + (FABL.discreteDerivative i) g
    theorem FABL.discreteDerivative_add {n : }
      (i : Fin n)
      (f g : FABL.SignCube n  ) :
      (FABL.discreteDerivative i) (f + g) =
        (FABL.discreteDerivative i) f +
          (FABL.discreteDerivative i) g
    O'Donnell, Definition 2.16: the discrete derivative is additive. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.discreteDerivative_setCoordinate {n : } (i : Fin n)
      (f : FABL.SignCube n  ) (x : FABL.SignCube n) (b : FABL.Sign) :
      (FABL.discreteDerivative i) f (FABL.setCoordinate x i b) =
        (FABL.discreteDerivative i) f x
    theorem FABL.discreteDerivative_setCoordinate
      {n : } (i : Fin n)
      (f : FABL.SignCube n  )
      (x : FABL.SignCube n) (b : FABL.Sign) :
      (FABL.discreteDerivative i) f
          (FABL.setCoordinate x i b) =
        (FABL.discreteDerivative i) f x
    O'Donnell, Definition 2.16: `D_i f` does not depend on coordinate `i`. 
Lemma2.2.6
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L∃∀N

Equation (2.1). If f:\{-1,1\}^n\to\{-1,1\} is Boolean-valued, then D_i f(x)= \begin{cases} 0 & \text{if coordinate }i\text{ is not pivotal for }f\text{ on }x,\\ \pm1 & \text{if coordinate }i\text{ is pivotal for }f\text{ on }x. \end{cases} Consequently, D_i f(x)^2 is the indicator that i is pivotal at x.

Lean code for Lemma2.2.62 declarations
  • defdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    def FABL.pivotalIndicator {n : } (f : FABL.BooleanFunction n) (i : Fin n)
      (x : FABL.SignCube n) : 
    def FABL.pivotalIndicator {n : }
      (f : FABL.BooleanFunction n) (i : Fin n)
      (x : FABL.SignCube n) : 
    The real-valued indicator that coordinate `i` is pivotal for `f` at `x`. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.sq_discreteDerivative_toReal_eq_pivotalIndicator {n : }
      (f : FABL.BooleanFunction n) (i : Fin n) (x : FABL.SignCube n) :
      (FABL.discreteDerivative i) f.toReal x ^ 2 =
        FABL.pivotalIndicator f i x
    theorem FABL.sq_discreteDerivative_toReal_eq_pivotalIndicator
      {n : } (f : FABL.BooleanFunction n)
      (i : Fin n) (x : FABL.SignCube n) :
      (FABL.discreteDerivative i) f.toReal x ^
          2 =
        FABL.pivotalIndicator f i x
    O'Donnell, Equation (2.1): the squared derivative of a Boolean function is the pivotality
    indicator. 
Definition2.2.7
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L∃∀N

Definition 2.17. For f:\{-1,1\}^n\to\mathbb R, the influence of coordinate i is \operatorname{Inf}_i[f] =\mathbb E_{\boldsymbol{x}\sim\{-1,1\}^n} [D_i f(\boldsymbol{x})^2] =\lVert D_i f\rVert_2^2. For Boolean-valued f, this agrees with Definition 2.13.

Lean code for Definition2.2.72 declarations
  • defdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    def FABL.influence {n : } (f : FABL.SignCube n  ) (i : Fin n) : 
    def FABL.influence {n : }
      (f : FABL.SignCube n  ) (i : Fin n) :
      
    O'Donnell, Definition 2.17: the real-valued influence `Inf_i[f] = 𝔼[(D_i f)²]`. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.booleanInfluence_eq_influence_toReal {n : }
      (f : FABL.BooleanFunction n) (i : Fin n) :
      FABL.booleanInfluence f i = FABL.influence f.toReal i
    theorem FABL.booleanInfluence_eq_influence_toReal
      {n : } (f : FABL.BooleanFunction n)
      (i : Fin n) :
      FABL.booleanInfluence f i =
        FABL.influence f.toReal i
    O'Donnell, Definitions 2.13 and 2.17 agree on Boolean-valued functions. 
Definition2.2.8
uses 1used by 1L∃∀N

Definition 2.18. A coordinate i\in[n] is relevant for f:\{-1,1\}^n\to\mathbb R if and only if \operatorname{Inf}_i[f]>0; equivalently, there is an x\in\{-1,1\}^n such that f(x^{(i\mapsto1)})\ne f(x^{(i\mapsto-1)}).

Lean code for Definition2.2.82 declarations
  • defdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    def FABL.IsRelevant {n : } (f : FABL.SignCube n  ) (i : Fin n) : Prop
    def FABL.IsRelevant {n : }
      (f : FABL.SignCube n  ) (i : Fin n) :
      Prop
    O'Donnell, Definition 2.18: coordinate `i` is relevant when its influence is positive. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.isRelevant_iff_exists_setCoordinate_ne {n : }
      (f : FABL.SignCube n  ) (i : Fin n) :
      FABL.IsRelevant f i 
         x, f (FABL.setCoordinate x i 1)  f (FABL.setCoordinate x i (-1))
    theorem FABL.isRelevant_iff_exists_setCoordinate_ne
      {n : } (f : FABL.SignCube n  )
      (i : Fin n) :
      FABL.IsRelevant f i 
         x,
          f (FABL.setCoordinate x i 1) 
            f (FABL.setCoordinate x i (-1))
    O'Donnell, Definition 2.18: relevance is equivalent to disagreement between the two
    coordinate restrictions. 
Proposition2.2.9
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L∃∀N

Proposition 2.19. Let f:\{-1,1\}^n\to\mathbb R have multilinear expansion f(x)=\sum_{S\subseteq[n]}\widehat f(S)x^S. Then D_i f(x) =\sum_{\substack{S\subseteq[n]\\i\in S}} \widehat f(S)x^{S\setminus\{i\}}. \tag{2.2}

Lean code for Proposition2.2.91 theorem
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.discreteDerivative_eq_fourier_sum {n : } (f : FABL.SignCube n  )
      (i : Fin n) (x : FABL.SignCube n) :
      (FABL.discreteDerivative i) f x =
         S with i  S, FABL.fourierCoeff f S * FABL.monomial (S.erase i) x
    theorem FABL.discreteDerivative_eq_fourier_sum
      {n : } (f : FABL.SignCube n  )
      (i : Fin n) (x : FABL.SignCube n) :
      (FABL.discreteDerivative i) f x =
         S with i  S,
          FABL.fourierCoeff f S *
            FABL.monomial (S.erase i) x
    O'Donnell, Proposition 2.19, Equation (2.2): the Fourier expansion of a discrete
    derivative. 
Theorem2.2.10
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L∃∀N

Theorem 2.20. For every f:\{-1,1\}^n\to\mathbb R and i\in[n], \operatorname{Inf}_i[f] =\sum_{\substack{S\subseteq[n]\\i\in S}}\widehat f(S)^2.

Lean code for Theorem2.2.101 theorem
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.influence_eq_sum_sq_fourierCoeff {n : } (f : FABL.SignCube n  )
      (i : Fin n) :
      FABL.influence f i =  S with i  S, FABL.fourierCoeff f S ^ 2
    theorem FABL.influence_eq_sum_sq_fourierCoeff
      {n : } (f : FABL.SignCube n  )
      (i : Fin n) :
      FABL.influence f i =
         S with i  S,
          FABL.fourierCoeff f S ^ 2
    O'Donnell, Theorem 2.20: influence is the Fourier weight on subsets containing coordinate
    `i`. 
Proposition2.2.11
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Proposition 2.21. If f:\{-1,1\}^n\to\{-1,1\} is monotone, then \operatorname{Inf}_i[f]=\widehat f(i) for every i\in[n], where \widehat f(i) abbreviates \widehat f(\{i\}).

Lean code for Proposition2.2.111 theorem
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.influence_eq_fourierCoeff_singleton_of_monotone {n : }
      (f : FABL.BooleanFunction n) (hf : Monotone f) (i : Fin n) :
      FABL.influence f.toReal i = FABL.fourierCoeff f.toReal {i}
    theorem FABL.influence_eq_fourierCoeff_singleton_of_monotone
      {n : } (f : FABL.BooleanFunction n)
      (hf : Monotone f) (i : Fin n) :
      FABL.influence f.toReal i =
        FABL.fourierCoeff f.toReal {i}
    O'Donnell, Proposition 2.21: for a monotone Boolean function, coordinate influence is its
    singleton Fourier coefficient. 
Proposition2.2.12
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used by 0L∃∀N

Proposition 2.22. If f:\{-1,1\}^n\to\{-1,1\} is transitive-symmetric and monotone, then \operatorname{Inf}_i[f]\le\frac1{\sqrt n} for every i\in[n].

Lean code for Proposition2.2.121 theorem
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.influence_le_one_div_sqrt_of_transitiveSymmetric_monotone {n : }
      (f : FABL.BooleanFunction n) (hsymm : FABL.IsTransitiveSymmetric f)
      (hf : Monotone f) (i : Fin n) : FABL.influence f.toReal i  1 / n
    theorem FABL.influence_le_one_div_sqrt_of_transitiveSymmetric_monotone
      {n : } (f : FABL.BooleanFunction n)
      (hsymm : FABL.IsTransitiveSymmetric f)
      (hf : Monotone f) (i : Fin n) :
      FABL.influence f.toReal i  1 / n
    O'Donnell, Proposition 2.22: every coordinate of a transitive-symmetric monotone Boolean
    function has influence at most `1 / √n`. 
Definition2.2.13
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L∃∀N

Definition 2.23. The ith expectation operator is the linear operator on f:\{-1,1\}^n\to\mathbb R defined by E_i f(x) =\mathbb E_{\boldsymbol{x}_i} [f(x_1,\ldots,x_{i-1},\boldsymbol{x}_i,x_{i+1},\ldots,x_n)], where \boldsymbol{x}_i is uniform on \{-1,1\}.

Lean code for Definition2.2.131 definition
  • defdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    def FABL.coordinateExpectation {n : } (i : Fin n) :
      (FABL.SignCube n  ) →ₗ[] FABL.SignCube n  
    def FABL.coordinateExpectation {n : }
      (i : Fin n) :
      (FABL.SignCube n  ) →ₗ[]
        FABL.SignCube n  
    O'Donnell, Definition 2.23: average over coordinate `i`, as an `ℝ`-linear map. 
Proposition2.2.14
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used by 1L∃∀N

Proposition 2.24. For every f:\{-1,1\}^n\to\mathbb R, \begin{aligned} E_i f(x)&=\frac{f(x^{(i\mapsto1)})+f(x^{(i\mapsto-1)})}{2},\\ E_i f(x)&=\sum_{\substack{S\subseteq[n]\\i\notin S}}\widehat f(S)x^S,\\ f(x)&=x_iD_i f(x)+E_i f(x). \end{aligned} Neither D_i f nor E_i f depends on x_i.

Lean code for Proposition2.2.144 theorems
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.coordinateExpectation_apply {n : } (i : Fin n)
      (f : FABL.SignCube n  ) (x : FABL.SignCube n) :
      (FABL.coordinateExpectation i) f x =
        (f (FABL.setCoordinate x i 1) + f (FABL.setCoordinate x i (-1))) / 2
    theorem FABL.coordinateExpectation_apply {n : }
      (i : Fin n) (f : FABL.SignCube n  )
      (x : FABL.SignCube n) :
      (FABL.coordinateExpectation i) f x =
        (f (FABL.setCoordinate x i 1) +
            f (FABL.setCoordinate x i (-1))) /
          2
    O'Donnell, Proposition 2.24: the coordinate expectation is the average of the two
    coordinate restrictions. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.coordinateExpectation_eq_fourier_sum {n : }
      (f : FABL.SignCube n  ) (i : Fin n) (x : FABL.SignCube n) :
      (FABL.coordinateExpectation i) f x =
         S with i  S, FABL.fourierCoeff f S * FABL.monomial S x
    theorem FABL.coordinateExpectation_eq_fourier_sum
      {n : } (f : FABL.SignCube n  )
      (i : Fin n) (x : FABL.SignCube n) :
      (FABL.coordinateExpectation i) f x =
         S with i  S,
          FABL.fourierCoeff f S *
            FABL.monomial S x
    O'Donnell, Proposition 2.24: the Fourier expansion of the coordinate expectation. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.eq_signValue_mul_discreteDerivative_add_coordinateExpectation
      {n : } (f : FABL.SignCube n  ) (i : Fin n) (x : FABL.SignCube n) :
      f x =
        FABL.signValue (x i) * (FABL.discreteDerivative i) f x +
          (FABL.coordinateExpectation i) f x
    theorem FABL.eq_signValue_mul_discreteDerivative_add_coordinateExpectation
      {n : } (f : FABL.SignCube n  )
      (i : Fin n) (x : FABL.SignCube n) :
      f x =
        FABL.signValue (x i) *
            (FABL.discreteDerivative i) f x +
          (FABL.coordinateExpectation i) f x
    O'Donnell, Proposition 2.24: `f = x_i D_i f + E_i f` pointwise. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.coordinateExpectation_setCoordinate {n : } (i : Fin n)
      (f : FABL.SignCube n  ) (x : FABL.SignCube n) (b : FABL.Sign) :
      (FABL.coordinateExpectation i) f (FABL.setCoordinate x i b) =
        (FABL.coordinateExpectation i) f x
    theorem FABL.coordinateExpectation_setCoordinate
      {n : } (i : Fin n)
      (f : FABL.SignCube n  )
      (x : FABL.SignCube n) (b : FABL.Sign) :
      (FABL.coordinateExpectation i) f
          (FABL.setCoordinate x i b) =
        (FABL.coordinateExpectation i) f x
    O'Donnell, Proposition 2.24: `E_i f` does not depend on coordinate `i`. 
Definition2.2.15
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L∃∀N

Definition 2.25. The ith coordinate Laplacian is L_i f=f-E_i f. The book warns that some sources use the negated convention E_i f-f.

Lean code for Definition2.2.151 definition
  • defdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    def FABL.coordinateLaplacian {n : } (i : Fin n) :
      (FABL.SignCube n  ) →ₗ[] FABL.SignCube n  
    def FABL.coordinateLaplacian {n : }
      (i : Fin n) :
      (FABL.SignCube n  ) →ₗ[]
        FABL.SignCube n  
    O'Donnell, Definition 2.25: the coordinate Laplacian `L_i = I - E_i`. 
Proposition2.2.16
Statement uses 5
Statement dependency previews
used by 1L∃∀N

Proposition 2.26. For every f:\{-1,1\}^n\to\mathbb R, \begin{aligned} L_i f(x)&=\frac{f(x)-f(x^{\oplus i})}{2},\\ L_i f(x)&=x_iD_i f(x) =\sum_{\substack{S\subseteq[n]\\i\in S}}\widehat f(S)x^S,\\ \langle f,L_i f\rangle&=\langle L_i f,L_i f\rangle =\operatorname{Inf}_i[f]. \end{aligned}

Lean code for Proposition2.2.165 theorems
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.coordinateLaplacian_eq_sub_flip_div_two {n : }
      (f : FABL.SignCube n  ) (i : Fin n) (x : FABL.SignCube n) :
      (FABL.coordinateLaplacian i) f x =
        (f x - f (FABL.flipCoordinate x i)) / 2
    theorem FABL.coordinateLaplacian_eq_sub_flip_div_two
      {n : } (f : FABL.SignCube n  )
      (i : Fin n) (x : FABL.SignCube n) :
      (FABL.coordinateLaplacian i) f x =
        (f x - f (FABL.flipCoordinate x i)) /
          2
    O'Donnell, Proposition 2.26: the coordinate Laplacian is half the difference across the
    dimension-`i` edge. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.coordinateLaplacian_eq_signValue_mul_discreteDerivative {n : }
      (f : FABL.SignCube n  ) (i : Fin n) (x : FABL.SignCube n) :
      (FABL.coordinateLaplacian i) f x =
        FABL.signValue (x i) * (FABL.discreteDerivative i) f x
    theorem FABL.coordinateLaplacian_eq_signValue_mul_discreteDerivative
      {n : } (f : FABL.SignCube n  )
      (i : Fin n) (x : FABL.SignCube n) :
      (FABL.coordinateLaplacian i) f x =
        FABL.signValue (x i) *
          (FABL.discreteDerivative i) f x
    O'Donnell, Proposition 2.26: the coordinate Laplacian is `x_i D_i f`. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.coordinateLaplacian_eq_fourier_sum {n : }
      (f : FABL.SignCube n  ) (i : Fin n) (x : FABL.SignCube n) :
      (FABL.coordinateLaplacian i) f x =
         S with i  S, FABL.fourierCoeff f S * FABL.monomial S x
    theorem FABL.coordinateLaplacian_eq_fourier_sum
      {n : } (f : FABL.SignCube n  )
      (i : Fin n) (x : FABL.SignCube n) :
      (FABL.coordinateLaplacian i) f x =
         S with i  S,
          FABL.fourierCoeff f S *
            FABL.monomial S x
    O'Donnell, Proposition 2.26: the coordinate Laplacian retains exactly the Fourier terms
    containing coordinate `i`. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.uniformInner_coordinateLaplacian_eq_influence {n : }
      (f : FABL.SignCube n  ) (i : Fin n) :
      FABL.uniformInner f ((FABL.coordinateLaplacian i) f) =
        FABL.influence f i
    theorem FABL.uniformInner_coordinateLaplacian_eq_influence
      {n : } (f : FABL.SignCube n  )
      (i : Fin n) :
      FABL.uniformInner f
          ((FABL.coordinateLaplacian i) f) =
        FABL.influence f i
    O'Donnell, Proposition 2.26: pairing a function with its coordinate Laplacian gives the
    coordinate influence. 
  • theoremdefined in FABL/Chapter02/InfluencesAndDerivatives/DiscreteDerivatives.lean
    complete
    theorem FABL.uniformInner_coordinateLaplacian_self_eq_influence {n : }
      (f : FABL.SignCube n  ) (i : Fin n) :
      FABL.uniformInner ((FABL.coordinateLaplacian i) f)
          ((FABL.coordinateLaplacian i) f) =
        FABL.influence f i
    theorem FABL.uniformInner_coordinateLaplacian_self_eq_influence
      {n : } (f : FABL.SignCube n  )
      (i : Fin n) :
      FABL.uniformInner
          ((FABL.coordinateLaplacian i) f)
          ((FABL.coordinateLaplacian i) f) =
        FABL.influence f i
    O'Donnell, Proposition 2.26: the Laplacian's normalized squared norm is the coordinate
    influence.