6.2. Existence of extremizers
The existence part of Theorem 1.4 of Cohn–Gonçalves (2019), used in Theorem 6.1.9, following their §3.2 with the modifications described at the beginning of the chapter.
(Cohn–Gonçalves 2019, (3.1).) For t > 0 let
\varphi_t(x) = \dfrac{e^{-t\pi|x|^2} - e^{-2t\pi|x|^2}}{t^{-d/2} - (2t)^{-d/2}}.
Lean code for Definition6.2.1●1 definition
Associated Lean declarations
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CohnElkies.gaussianDifference[complete]
-
CohnElkies.gaussianDifference[complete]
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defdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
def CohnElkies.gaussianDifference (d : ℕ) (t : ℝ) (x : CohnElkies.Euclidean d) : ℝ
def CohnElkies.gaussianDifference (d : ℕ) (t : ℝ) (x : CohnElkies.Euclidean d) : ℝ
Cohn–Gonçalves (3.1): the Gaussian difference `φ_t(x) = (e^{-tπ|x|²} - e^{-2tπ|x|²}) / (t^{-d/2} - (2t)^{-d/2})`.
With \varphi_t as in Definition 6.2.1, let
\psi_t = \varphi_t - \widehat{\varphi_t}.
Lean code for Definition6.2.2●1 definition
Associated Lean declarations
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CohnElkies.gaussianPerturbation[complete]
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CohnElkies.gaussianPerturbation[complete]
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defdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
def CohnElkies.gaussianPerturbation (d : ℕ) (t : ℝ) (x : CohnElkies.Euclidean d) : ℝ
def CohnElkies.gaussianPerturbation (d : ℕ) (t : ℝ) (x : CohnElkies.Euclidean d) : ℝ
`ψ_t = φ_t - 𝓕φ_t` (Cohn–Gonçalves, proof of Lemma 3.1 and §3.3): `𝓕ψ_t = -ψ_t`, `ψ_t(0) = -1`, and `ψ_t > 0` outside the ball of radius `√(t d log 2 / π)`.
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CohnElkies.fourier_gaussianDifference[complete] -
CohnElkies.gaussianDifference_nonneg[complete] -
CohnElkies.gaussianDifference_apply_zero[complete] -
CohnElkies.fourier_gaussianDifference_zero[complete] -
CohnElkies.fourier_fourier_gaussianDifference[complete] -
CohnElkies.fourierGaussianDifference_neg_of_lt[complete]
Let d \ge 1 and t > 0, with \varphi_t as in Definition 6.2.1.
Then
\widehat{\varphi_t}(\xi) = \dfrac{t^{-d/2}e^{-\pi|\xi|^2/t} - (2t)^{-d/2}e^{-\pi|\xi|^2/(2t)}}{t^{-d/2} - (2t)^{-d/2}},
\varphi_t \ge 0, \varphi_t(0) = 0, \widehat{\varphi_t}(0) = 1,
\widehat{\widehat{\varphi_t}} = \varphi_t, and \widehat{\varphi_t}(\xi) < 0 whenever
|\xi|^2 > t\,d\log 2/\pi (nonpositive when \ge).
Lean code for Lemma6.2.3●6 theorems
Associated Lean declarations
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CohnElkies.fourier_gaussianDifference[complete]
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CohnElkies.gaussianDifference_nonneg[complete]
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CohnElkies.gaussianDifference_apply_zero[complete]
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CohnElkies.fourier_gaussianDifference_zero[complete]
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CohnElkies.fourier_fourier_gaussianDifference[complete]
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CohnElkies.fourierGaussianDifference_neg_of_lt[complete]
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CohnElkies.fourier_gaussianDifference[complete] -
CohnElkies.gaussianDifference_nonneg[complete] -
CohnElkies.gaussianDifference_apply_zero[complete] -
CohnElkies.fourier_gaussianDifference_zero[complete] -
CohnElkies.fourier_fourier_gaussianDifference[complete] -
CohnElkies.fourierGaussianDifference_neg_of_lt[complete]
-
theoremdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
theorem CohnElkies.fourier_gaussianDifference {d : ℕ} {t : ℝ} (ht : 0 < t) (ξ : CohnElkies.Euclidean d) : FourierTransform.fourier (fun x ↦ ↑(CohnElkies.gaussianDifference d t x)) ξ = ↑(CohnElkies.fourierGaussianDifference d t ξ)
theorem CohnElkies.fourier_gaussianDifference {d : ℕ} {t : ℝ} (ht : 0 < t) (ξ : CohnElkies.Euclidean d) : FourierTransform.fourier (fun x ↦ ↑(CohnElkies.gaussianDifference d t x)) ξ = ↑(CohnElkies.fourierGaussianDifference d t ξ)
`𝓕φ_t(ξ) = (t^{-d/2} e^{-π|ξ|²/t} - (2t)^{-d/2} e^{-π|ξ|²/(2t)}) / (t^{-d/2} - (2t)^{-d/2})`. -
theoremdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
theorem CohnElkies.gaussianDifference_nonneg {d : ℕ} (hd : 0 < d) {t : ℝ} (ht : 0 < t) (x : CohnElkies.Euclidean d) : 0 ≤ CohnElkies.gaussianDifference d t x
theorem CohnElkies.gaussianDifference_nonneg {d : ℕ} (hd : 0 < d) {t : ℝ} (ht : 0 < t) (x : CohnElkies.Euclidean d) : 0 ≤ CohnElkies.gaussianDifference d t x
`φ_t ≥ 0` for `t > 0`, `d ≥ 1`.
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theoremdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
theorem CohnElkies.gaussianDifference_apply_zero {d : ℕ} (t : ℝ) : CohnElkies.gaussianDifference d t 0 = 0
theorem CohnElkies.gaussianDifference_apply_zero {d : ℕ} (t : ℝ) : CohnElkies.gaussianDifference d t 0 = 0
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theoremdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
theorem CohnElkies.fourier_gaussianDifference_zero {d : ℕ} (hd : 0 < d) {t : ℝ} (ht : 0 < t) : FourierTransform.fourier (fun x ↦ ↑(CohnElkies.gaussianDifference d t x)) 0 = 1
theorem CohnElkies.fourier_gaussianDifference_zero {d : ℕ} (hd : 0 < d) {t : ℝ} (ht : 0 < t) : FourierTransform.fourier (fun x ↦ ↑(CohnElkies.gaussianDifference d t x)) 0 = 1
`𝓕φ_t(0) = 1`.
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theoremdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
theorem CohnElkies.fourier_fourier_gaussianDifference {d : ℕ} {t : ℝ} (ht : 0 < t) (ξ : CohnElkies.Euclidean d) : FourierTransform.fourier (FourierTransform.fourier fun x ↦ ↑(CohnElkies.gaussianDifference d t x)) ξ = ↑(CohnElkies.gaussianDifference d t ξ)
theorem CohnElkies.fourier_fourier_gaussianDifference {d : ℕ} {t : ℝ} (ht : 0 < t) (ξ : CohnElkies.Euclidean d) : FourierTransform.fourier (FourierTransform.fourier fun x ↦ ↑(CohnElkies.gaussianDifference d t x)) ξ = ↑(CohnElkies.gaussianDifference d t ξ)
`𝓕 (𝓕 φ_t) = φ_t` (`φ_t` is even).
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theoremdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
theorem CohnElkies.fourierGaussianDifference_neg_of_lt {d : ℕ} (hd : 0 < d) {t : ℝ} (ht : 0 < t) {ξ : CohnElkies.Euclidean d} (h : t * ↑d * Real.log 2 / Real.pi < ‖ξ‖ ^ 2) : CohnElkies.fourierGaussianDifference d t ξ < 0
theorem CohnElkies.fourierGaussianDifference_neg_of_lt {d : ℕ} (hd : 0 < d) {t : ℝ} (ht : 0 < t) {ξ : CohnElkies.Euclidean d} (h : t * ↑d * Real.log 2 / Real.pi < ‖ξ‖ ^ 2) : CohnElkies.fourierGaussianDifference d t ξ < 0
`𝓕φ_t(ξ) < 0` when `|ξ|² > t d log 2 / π` (Cohn–Gonçalves, proof of Lemma 3.1).
The Fourier transform of e^{-b\pi|x|^2} is b^{-d/2}e^{-\pi|\xi|^2/b}, which gives the formula;
taking logarithms, t^{-d/2}e^{-\pi|\xi|^2/t} < (2t)^{-d/2}e^{-\pi|\xi|^2/(2t)} if and only
if \pi|\xi|^2/(2t) > (d/2)\log 2, i.e. |\xi|^2 > t\,d\log 2/\pi. Applying the transform
formula twice gives \widehat{\widehat{\varphi_t}} = \varphi_t.
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CohnElkies.fourier_gaussianPerturbation[complete] -
CohnElkies.gaussianPerturbation_apply_zero[complete] -
CohnElkies.gaussianPerturbation_pos[complete]
Let d \ge 1 and t > 0, with \psi_t as in Definition 6.2.2.
Then \widehat{\psi_t} = -\psi_t, \psi_t(0) = -1, and \psi_t > 0 outside the ball of
radius \sqrt{t\,d\log 2/\pi}.
Lean code for Lemma6.2.4●3 theorems
Associated Lean declarations
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CohnElkies.fourier_gaussianPerturbation[complete]
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CohnElkies.gaussianPerturbation_apply_zero[complete]
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CohnElkies.gaussianPerturbation_pos[complete]
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CohnElkies.fourier_gaussianPerturbation[complete] -
CohnElkies.gaussianPerturbation_apply_zero[complete] -
CohnElkies.gaussianPerturbation_pos[complete]
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theoremdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
theorem CohnElkies.fourier_gaussianPerturbation {d : ℕ} {t : ℝ} (ht : 0 < t) (ξ : CohnElkies.Euclidean d) : FourierTransform.fourier (fun x ↦ ↑(CohnElkies.gaussianPerturbation d t x)) ξ = -↑(CohnElkies.gaussianPerturbation d t ξ)
theorem CohnElkies.fourier_gaussianPerturbation {d : ℕ} {t : ℝ} (ht : 0 < t) (ξ : CohnElkies.Euclidean d) : FourierTransform.fourier (fun x ↦ ↑(CohnElkies.gaussianPerturbation d t x)) ξ = -↑(CohnElkies.gaussianPerturbation d t ξ)
`𝓕ψ_t = -ψ_t`.
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theoremdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
theorem CohnElkies.gaussianPerturbation_apply_zero {d : ℕ} (hd : 0 < d) {t : ℝ} (ht : 0 < t) : CohnElkies.gaussianPerturbation d t 0 = -1
theorem CohnElkies.gaussianPerturbation_apply_zero {d : ℕ} (hd : 0 < d) {t : ℝ} (ht : 0 < t) : CohnElkies.gaussianPerturbation d t 0 = -1
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theoremdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
theorem CohnElkies.gaussianPerturbation_pos {d : ℕ} (hd : 0 < d) {t : ℝ} (ht : 0 < t) {x : CohnElkies.Euclidean d} (h : t * ↑d * Real.log 2 / Real.pi < ‖x‖ ^ 2) : 0 < CohnElkies.gaussianPerturbation d t x
theorem CohnElkies.gaussianPerturbation_pos {d : ℕ} (hd : 0 < d) {t : ℝ} (ht : 0 < t) {x : CohnElkies.Euclidean d} (h : t * ↑d * Real.log 2 / Real.pi < ‖x‖ ^ 2) : 0 < CohnElkies.gaussianPerturbation d t x
`ψ_t > 0` when `|ξ|² > t d log 2 / π`.
Immediate from Lemma 6.2.3:
\widehat{\psi_t} = \widehat{\varphi_t} - \varphi_t = -\psi_t,
\psi_t(0) = 0 - 1, and \psi_t = \varphi_t - \widehat{\varphi_t} > 0 where
\widehat{\varphi_t} < 0.
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CohnElkies.originCorrection[complete] -
CohnElkies.originCorrection_nonneg_outside[complete] -
CohnElkies.originCorrection_eq_of_zero[complete]
(Cohn–Gonçalves 2019, Lemma 3.1, last paragraph.) Let d \ge 1, R > 0, and let
g \in L^1(\mathbb{R}^d;\mathbb{R}) satisfy \widehat g = -g pointwise, g \ne 0,
g \ge 0 on \{|x| \ge R\} and g(0) \ge 0. With t = \pi R^2/(d\log 2) and \psi_t as in
Definition 6.2.2, the function h = g + g(0)\psi_t belongs to
\mathcal{E}_-(d) (Definition 1.2.1) and satisfies h \ge 0 on
\{|x| \ge R\}, so r(h) \le R; moreover h = g if g(0) = 0.
Lean code for Lemma6.2.5●3 declarations
Associated Lean declarations
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CohnElkies.originCorrection[complete]
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CohnElkies.originCorrection_nonneg_outside[complete]
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CohnElkies.originCorrection_eq_of_zero[complete]
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CohnElkies.originCorrection[complete] -
CohnElkies.originCorrection_nonneg_outside[complete] -
CohnElkies.originCorrection_eq_of_zero[complete]
-
defdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
def CohnElkies.originCorrection {d : ℕ} (hd : 0 < d) (g : CohnElkies.Euclidean d → ℝ) (hint : MeasureTheory.Integrable g MeasureTheory.volume) (hfour : ∀ (ξ : CohnElkies.Euclidean d), FourierTransform.fourier (fun x ↦ ↑(g x)) ξ = -↑(g ξ)) (hne : g ≠ 0) {R : ℝ} (hR : 0 < R) (hpos : ∀ (x : CohnElkies.Euclidean d), R ≤ ‖x‖ → 0 ≤ g x) (h0 : 0 ≤ g 0) : CohnElkies.SignEigenfunction d (-1)
def CohnElkies.originCorrection {d : ℕ} (hd : 0 < d) (g : CohnElkies.Euclidean d → ℝ) (hint : MeasureTheory.Integrable g MeasureTheory.volume) (hfour : ∀ (ξ : CohnElkies.Euclidean d), FourierTransform.fourier (fun x ↦ ↑(g x)) ξ = -↑(g ξ)) (hne : g ≠ 0) {R : ℝ} (hR : 0 < R) (hpos : ∀ (x : CohnElkies.Euclidean d), R ≤ ‖x‖ → 0 ≤ g x) (h0 : 0 ≤ g 0) : CohnElkies.SignEigenfunction d (-1)
Cohn–Gonçalves, proof of Lemma 3.1 (last paragraph): the corrected function `h = g + g(0) (φ_t - 𝓕φ_t)`, `t d log 2 / π = R²`, is a sign eigenfunction of eigenvalue `-1`: `𝓕 h = -h` (since `𝓕 g = -g`, `𝓕𝓕φ_t = φ_t`), `h(0) = g(0) + g(0)(0 - 1) = 0`, and `h ≠ 0` (`h = g` if `g(0) = 0`; `h(x) > g(x) ≥ 0` for `|x| > R` if `g(0) > 0`).
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theoremdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
theorem CohnElkies.originCorrection_nonneg_outside {d : ℕ} (hd : 0 < d) (g : CohnElkies.Euclidean d → ℝ) (hint : MeasureTheory.Integrable g MeasureTheory.volume) (hfour : ∀ (ξ : CohnElkies.Euclidean d), FourierTransform.fourier (fun x ↦ ↑(g x)) ξ = -↑(g ξ)) (hne : g ≠ 0) {R : ℝ} (hR : 0 < R) (hpos : ∀ (x : CohnElkies.Euclidean d), R ≤ ‖x‖ → 0 ≤ g x) (h0 : 0 ≤ g 0) (x : CohnElkies.Euclidean d) (hx : R ≤ ‖x‖) : 0 ≤ (CohnElkies.originCorrection hd g hint hfour hne hR hpos h0).toFun x
theorem CohnElkies.originCorrection_nonneg_outside {d : ℕ} (hd : 0 < d) (g : CohnElkies.Euclidean d → ℝ) (hint : MeasureTheory.Integrable g MeasureTheory.volume) (hfour : ∀ (ξ : CohnElkies.Euclidean d), FourierTransform.fourier (fun x ↦ ↑(g x)) ξ = -↑(g ξ)) (hne : g ≠ 0) {R : ℝ} (hR : 0 < R) (hpos : ∀ (x : CohnElkies.Euclidean d), R ≤ ‖x‖ → 0 ≤ g x) (h0 : 0 ≤ g 0) (x : CohnElkies.Euclidean d) (hx : R ≤ ‖x‖) : 0 ≤ (CohnElkies.originCorrection hd g hint hfour hne hR hpos h0).toFun x
`h ≥ 0` outside the ball of radius `R`.
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theoremdefined in CohnElkies/SignUncertainty/OriginCorrection.leancomplete
theorem CohnElkies.originCorrection_eq_of_zero {d : ℕ} (hd : 0 < d) (g : CohnElkies.Euclidean d → ℝ) (hint : MeasureTheory.Integrable g MeasureTheory.volume) (hfour : ∀ (ξ : CohnElkies.Euclidean d), FourierTransform.fourier (fun x ↦ ↑(g x)) ξ = -↑(g ξ)) (hne : g ≠ 0) {R : ℝ} (hR : 0 < R) (hpos : ∀ (x : CohnElkies.Euclidean d), R ≤ ‖x‖ → 0 ≤ g x) (h0 : 0 ≤ g 0) (h0' : g 0 = 0) : (CohnElkies.originCorrection hd g hint hfour hne hR hpos h0).toFun = g
theorem CohnElkies.originCorrection_eq_of_zero {d : ℕ} (hd : 0 < d) (g : CohnElkies.Euclidean d → ℝ) (hint : MeasureTheory.Integrable g MeasureTheory.volume) (hfour : ∀ (ξ : CohnElkies.Euclidean d), FourierTransform.fourier (fun x ↦ ↑(g x)) ξ = -↑(g ξ)) (hne : g ≠ 0) {R : ℝ} (hR : 0 < R) (hpos : ∀ (x : CohnElkies.Euclidean d), R ≤ ‖x‖ → 0 ≤ g x) (h0 : 0 ≤ g 0) (h0' : g 0 = 0) : (CohnElkies.originCorrection hd g hint hfour hne hR hpos h0).toFun = g
`h = g` when `g(0) = 0`.
By Lemma 6.2.4, \widehat h = -g + g(0)(-\psi_t) = -h,
h(0) = g(0) + g(0)\psi_t(0) = 0, and for |x| \ge R one has |x|^2 \ge t\,d\log 2/\pi, so
\psi_t(x) \ge 0 and h(x) \ge g(x) \ge 0. If g(0) > 0 then h(x) > g(x) \ge 0 for
|x| > R, so h \ne 0; if g(0) = 0 then h = g \ne 0.
For every d \ge 1 and \varsigma = \pm 1, \mathsf{A}_\varsigma(d) > 0
(Definition 1.2.3).
Lean code for Lemma6.2.6●1 theorem
Associated Lean declarations
-
CohnElkies.signUncertaintyConstant_pos[complete]
-
CohnElkies.signUncertaintyConstant_pos[complete]
-
theoremdefined in CohnElkies/SignUncertainty/Finiteness.leancomplete
theorem CohnElkies.signUncertaintyConstant_pos {d : ℕ} (hd : 0 < d) (ς : ℤˣ) : 0 < CohnElkies.signUncertaintyConstant ς d
theorem CohnElkies.signUncertaintyConstant_pos {d : ℕ} (hd : 0 < d) (ς : ℤˣ) : 0 < CohnElkies.signUncertaintyConstant ς d
`A_ς(d) > 0` for `d ≥ 1` and both signs (Cohn–Gonçalves §3.1): no sign eigenfunction is nonnegative outside a ball of volume `< ½`.
(Cohn–Gonçalves 2019, §3.1.) If g \in \mathcal{E}_\varsigma(d) with \|g\|_1 = 1 is
nonnegative outside B_\rho, then \int g = \widehat g(0) = \varsigma g(0) = 0 gives
\int g_- = \tfrac12, while g_- \le |g| \le \|\widehat g\|_1 = 1 vanishes outside B_\rho,
so \tfrac12 \le \operatorname{vol}(B_\rho); choosing \rho with
\operatorname{vol}(B_\rho) < \tfrac12 shows \mathsf{A}_\varsigma(d) \ge \rho > 0.
For every d \ge 1, with \psi_t as in Definition 6.2.2,
G = \psi_{1/4} - \psi_{1/2} belongs to \mathcal{E}_-(d) and is positive outside a ball, so
r(G) < \infty.
Lean code for Lemma6.2.7●3 declarations
Associated Lean declarations
-
defdefined in CohnElkies/SignUncertainty/Finiteness.leancomplete
def CohnElkies.explicitSignEigenfunction {d : ℕ} (hd : 0 < d) : CohnElkies.SignEigenfunction d (-1)
def CohnElkies.explicitSignEigenfunction {d : ℕ} (hd : 0 < d) : CohnElkies.SignEigenfunction d (-1)
Cohn–Gonçalves, §3.3: `G = ψ_{1/4} - ψ_{1/2} ∈ 𝓔₋(d)`: `𝓕 G = -G` (as `𝓕ψ_t = -ψ_t`), `G(0) = -1 - (-1) = 0`, `G` is integrable, and `G ≠ 0` since `G > 0` outside the ball of radius `R(d)`. -
theoremdefined in CohnElkies/SignUncertainty/Finiteness.leancomplete
theorem CohnElkies.explicitEigenfunction_pos_of_le {d : ℕ} (hd : 0 < d) {x : CohnElkies.Euclidean d} (hx : CohnElkies.explicitEigenfunctionRadius d ≤ ‖x‖) : 0 < CohnElkies.explicitEigenfunction d x
theorem CohnElkies.explicitEigenfunction_pos_of_le {d : ℕ} (hd : 0 < d) {x : CohnElkies.Euclidean d} (hx : CohnElkies.explicitEigenfunctionRadius d ≤ ‖x‖) : 0 < CohnElkies.explicitEigenfunction d x
`G(x) > 0` for `|x| ≥ R(d)`: then `|x|² ≥ (√L + 1)² > L = (4/π) log (1 + D/D' + 2^d)`.
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theoremdefined in CohnElkies/SignUncertainty/Finiteness.leancomplete
theorem CohnElkies.signRadius_explicitSignEigenfunction_lt_top {d : ℕ} (hd : 0 < d) : CohnElkies.signRadius (CohnElkies.explicitSignEigenfunction hd).toFun < ⊤
theorem CohnElkies.signRadius_explicitSignEigenfunction_lt_top {d : ℕ} (hd : 0 < d) : CohnElkies.signRadius (CohnElkies.explicitSignEigenfunction hd).toFun < ⊤
`r(G) < ∞`.
G satisfies \widehat G = -G and G(0) = -1 - (-1) = 0
(Lemma 6.2.4). Writing E_b(x) = e^{-\pi b|x|^2},
D = 2^d - 2^{d/2}, D' = 2^{d/2} - 1,
\psi_{1/4} = (E_{1/4} - E_{1/2} - 2^dE_4 + 2^{d/2}E_2)/D and \psi_{1/2} = (E_{1/2} - 2^{d/2}E_2)/D',
so that G \ge E_{1/4}\bigl(1 - e^{-\pi|x|^2/4}(1 + D/D' + 2^d)\bigr)/D > 0 once
|x|^2 > (4/\pi)\log(1 + D/D' + 2^d); hence G \ne 0 and G \in \mathcal{E}_-(d).
For every d \ge 1, \mathsf{A}_-(d) < \infty (Definition 1.2.3).
Lean code for Lemma6.2.8●1 theorem
Associated Lean declarations
-
theoremdefined in CohnElkies/SignUncertainty/Finiteness.leancomplete
theorem CohnElkies.signUncertaintyConstant_neg_one_lt_top {d : ℕ} (hd : 0 < d) : CohnElkies.signUncertaintyConstant (-1) d < ⊤
theorem CohnElkies.signUncertaintyConstant_neg_one_lt_top {d : ℕ} (hd : 0 < d) : CohnElkies.signUncertaintyConstant (-1) d < ⊤
`A₋(d) < ∞` for `d ≥ 1`: `A₋(d) ≤ r(G) < ∞` for the explicit `G ∈ 𝓔₋(d)`.
\mathsf{A}_-(d) \le r(G) < \infty for the explicit G of Lemma 6.2.7.
Every bounded sequence (x_n) in a separable Hilbert space E has a weakly convergent
subsequence: there are \varphi strictly increasing and y \in E with
\|y\| \le \sup_n\|x_n\| and \langle x_{\varphi(n)}, z\rangle \to \langle y, z\rangle for
every z \in E.
Lean code for Lemma6.2.9●1 theorem
Associated Lean declarations
-
complete
theorem InnerProductSpace.tendsto_subseq_inner_left_of_norm_le.{u_2, u_3} (𝕜 : Type u_2) {E : Type u_3} [RCLike 𝕜] [NormedAddCommGroup E] [InnerProductSpace 𝕜 E] [CompleteSpace E] [TopologicalSpace.SeparableSpace E] {x : ℕ → E} {C : ℝ} (hx : ∀ (n : ℕ), ‖x n‖ ≤ C) : ∃ φ y, StrictMono φ ∧ ‖y‖ ≤ C ∧ ∀ (z : E), Filter.Tendsto (fun n ↦ inner 𝕜 (x (φ n)) z) Filter.atTop (nhds (inner 𝕜 y z))
theorem InnerProductSpace.tendsto_subseq_inner_left_of_norm_le.{u_2, u_3} (𝕜 : Type u_2) {E : Type u_3} [RCLike 𝕜] [NormedAddCommGroup E] [InnerProductSpace 𝕜 E] [CompleteSpace E] [TopologicalSpace.SeparableSpace E] {x : ℕ → E} {C : ℝ} (hx : ∀ (n : ℕ), ‖x n‖ ≤ C) : ∃ φ y, StrictMono φ ∧ ‖y‖ ≤ C ∧ ∀ (z : E), Filter.Tendsto (fun n ↦ inner 𝕜 (x (φ n)) z) Filter.atTop (nhds (inner 𝕜 y z))
Every bounded sequence in a separable Hilbert space has a weakly convergent subsequence: if `‖x n‖ ≤ C` for all `n`, there are a strictly increasing `φ` and `y` with `‖y‖ ≤ C` such that `⟪x (φ n), z⟫` converges to `⟪y, z⟫` for every `z`.
By the Fréchet–Riesz theorem the functionals \langle x_n, \cdot\rangle lie in a closed ball of
the dual E^*, which is weak-star sequentially compact for separable E (sequential
Banach–Alaoglu, Mathlib's WeakDual.isSeqCompact_closedBall); transporting the limit functional
back along the Riesz isometry gives y.
Let V be a nontrivial finite-dimensional real inner product space, c \in \mathbb{C}\setminus\{0\}
and R \ge 0. There is \kappa > 0 such that every integrable f : V \to \mathbb{C} with
\widehat f = cf pointwise and \|f\|_1 = 1 satisfies \int_{|x| > R}|f| \ge \kappa.
Lean code for Lemma6.2.10●1 theorem
Associated Lean declarations
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theoremdefined in CohnElkiesForMathlib/Analysis/Fourier/EigenfunctionConcentration.leancomplete
theorem Real.exists_pos_le_setIntegral_norm_compl_closedBall_of_fourier_eq_mul.{u_1} {V : Type u_1} [NormedAddCommGroup V] [InnerProductSpace ℝ V] [FiniteDimensional ℝ V] [MeasurableSpace V] [BorelSpace V] [Nontrivial V] {c : ℂ} (hc : c ≠ 0) (R : ℝ) : ∃ κ, 0 < κ ∧ ∀ (f : V → ℂ), MeasureTheory.Integrable f MeasureTheory.volume → (∀ (ξ : V), FourierTransform.fourier f ξ = c * f ξ) → ∫ (x : V), ‖f x‖ = 1 → κ ≤ ∫ (x : V) in (Metric.closedBall 0 R)ᶜ, ‖f x‖
theorem Real.exists_pos_le_setIntegral_norm_compl_closedBall_of_fourier_eq_mul.{u_1} {V : Type u_1} [NormedAddCommGroup V] [InnerProductSpace ℝ V] [FiniteDimensional ℝ V] [MeasurableSpace V] [BorelSpace V] [Nontrivial V] {c : ℂ} (hc : c ≠ 0) (R : ℝ) : ∃ κ, 0 < κ ∧ ∀ (f : V → ℂ), MeasureTheory.Integrable f MeasureTheory.volume → (∀ (ξ : V), FourierTransform.fourier f ξ = c * f ξ) → ∫ (x : V), ‖f x‖ = 1 → κ ≤ ∫ (x : V) in (Metric.closedBall 0 R)ᶜ, ‖f x‖
**Eigenfunctions of the Fourier transform do not concentrate on a ball**: for `c ≠ 0` and `R : ℝ` there is `κ > 0` such that every integrable `f : V → ℂ` with `𝓕 f ξ = c * f ξ` for all `ξ` and `∫ ‖f‖ = 1` has `L¹` mass at least `κ` outside the closed ball of radius `R`.
Suppose not: there are such f_n with \delta_n = \int_{B^c}|f_n| \to 0, B the closed ball of
radius R. Each f_n = c^{-1}\widehat{f_n} is continuous with |f_n| \le |c|^{-1}. The
truncations 1_Bf_n are bounded in L^2, so by Lemma 6.2.9
a subsequence converges weakly to some g \in L^2; testing against the kernels
1_B(x)e^{2\pi i\langle x,\xi\rangle} shows \widehat{1_Bf_n}(\xi) \to \widehat{1_Bg}(\xi) for
every \xi, and |\widehat{f_n} - \widehat{1_Bf_n}| \le \delta_n, so f_n \to G := c^{-1}\widehat{1_Bg}
pointwise, with G continuous. Bounded convergence on B gives
\int_B|G| = \lim(1 - \delta_n) = 1; Fatou on B^c gives G = 0 a.e., hence everywhere, off
B; and bounded convergence on B again gives
\widehat G(\xi) = \lim\int_B e^{-2\pi i\langle x,\xi\rangle}f_n = \lim(\widehat{f_n}(\xi) + O(\delta_n)) = cG(\xi).
Thus G and \widehat G are compactly supported and G \ne 0, contradicting
Lemma 3.2.12.
(Cohn–Gonçalves 2019, Theorem 1.4, existence part.) For every d \ge 1 there exists
g \in \mathcal{E}_-(d) with r(g) = \mathsf{A}_-(d) (Definition 1.2.3).
Lean code for Theorem6.2.11●1 theorem
Associated Lean declarations
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theoremdefined in CohnElkies/SignUncertainty/AppendixA.leancomplete
theorem CohnElkies.exists_signRadius_eq_signUncertaintyConstant_neg_one {d : ℕ} (hd : 0 < d) : ∃ g, CohnElkies.signRadius g.toFun = CohnElkies.signUncertaintyConstant (-1) d
theorem CohnElkies.exists_signRadius_eq_signUncertaintyConstant_neg_one {d : ℕ} (hd : 0 < d) : ∃ g, CohnElkies.signRadius g.toFun = CohnElkies.signUncertaintyConstant (-1) d
Cohn–Gonçalves 2019, Theorem 1.4 (existence): `A₋(d)` is attained by some `g ∈ 𝓔₋(d)`.
By Lemma 6.2.6 and
Lemma 6.2.8, 0 < a := \mathsf{A}_-(d) < \infty.
Pick f_n \in \mathcal{E}_-(d) with r(f_n) \le a + 1/(n+1), normalized so that
\|f_n\|_1 = 1; then |f_n| = |\widehat{f_n}| \le 1, \int f_n = 0, and f_n \ge 0 on
\{|x| \ge a + 1/(n+1)\}. By Lemma 6.2.10 with
R = a + 1 there is \kappa > 0 with \int_{|x| \le a+1} f_n = -\int_{|x| > a+1} f_n \le -\kappa
for all n. Since \|f_n\|_2^2 \le \|f_n\|_\infty\|f_n\|_1 \le 1,
Lemma 6.2.9 gives a subsequence converging weakly in L^2 to
some g. Testing the weak convergence against bounded compactly supported functions shows:
g \in L^1 with \int_K|g| \le 1 for every compact K (test with 1_K\operatorname{sign} g);
\int_{|x| \le a+1} g \le -\kappa, so g \ne 0; \int_K g \le 0 for all balls K \supseteq B_{a+1},
hence \int g \le 0; and g \ge 0 a.e. on \{|x| > a\} (test with indicators of
\{a + 1/(m+1) \le |x| \le m+1\} \cap \{g < 0\}). Testing against Schwartz functions \Phi and
using \int\widehat u\,\Phi = \int u\,\widehat\Phi for u \in L^1 yields
\int(\widehat g + g)\Phi = 0 for all smooth compactly supported \Phi, so \widehat g = -g
a.e. The continuous function G = -\widehat g therefore satisfies G = g a.e., \widehat G = -G
pointwise, G \ne 0, G(0) = -\int g \ge 0, and G \ge 0 on \{|x| \ge a\} (a.e. on the open
exterior, hence everywhere by continuity). Finally Lemma 6.2.5
with R = a produces h \in \mathcal{E}_-(d) with r(h) \le a, and r(h) \ge \mathsf{A}_-(d) = a
by definition of the infimum.
This differs from the proof of Cohn–Gonçalves in three places: their uniform negative-mass bound
\int_{B} f_n \le K < 0 is deduced from Nazarov's uncertainty principle in Jaming's form (or
from the Amrein–Berthier inequality), here from the compactness lemma
Lemma 6.2.10; they pass from weak to almost-everywhere
and L^2 convergence by Mazur's lemma before applying Fatou's lemma, whereas here the weak limit is
only tested against explicit L^2 and smooth compactly supported functions; and they normalize
the minimizing sequence by their Lemma 3.1 and infer f(0) = 0 for the limit from minimality,
whereas here every element of \mathcal{E}_-(d) already has \widehat g = -g and g(0) = 0,
and the origin correction is applied once, to the limit.