This should roughly contain the contents of chapter 9.
Lemma 9.0.2
The Hardy-Littlewood maximal function w.r.t. a collection of balls 𝓑. M_{𝓑, p} in the blueprint.
Equations
- maximalFunction μ 𝓑 c r p u x = (⨆ i ∈ 𝓑, (Metric.ball (c i) (r i)).indicator (fun (x : X) => ⨍⁻ (y : X) in Metric.ball (c i) (r i), ‖u y‖ₑ ^ p ∂μ) x) ^ p⁻¹
Instances For
The Hardy-Littlewood maximal function w.r.t. a collection of balls 𝓑 with exponent 1. M_𝓑 in the blueprint.
Equations
- MB μ 𝓑 c r u x = maximalFunction μ 𝓑 c r 1 u x
Instances For
Special case of equation (2.0.44). The proof is given between (9.0.12) and (9.0.34). Use the real interpolation theorem instead of following the blueprint.
Equation (2.0.44). The proof is given between (9.0.34) and (9.0.36).
This is a special case of hasStrongType_maximalFunction below, which doesn't have the assumption
hR (but uses this result in its proof).
Equations
- maximalFunction_seq μ h𝓑 c r q v k z = maximalFunction μ (tr h𝓑 k) c r q v z
Instances For
hasStrongType_maximalFunction minus the assumption hR.
A proof for basically this result is given in Chapter 9, everything following after equation
(9.0.36).
hasStrongType_maximalFunction minus the assumption hR, but where p₁ = p₂ is possible and
we only conclude a weak-type estimate.
The transformation M characterized in Proposition 2.0.6.
p is 1 in the blueprint, and globalMaximalFunction μ p u = (M (u ^ p)) ^ p⁻¹
Equations
Instances For
Equation (2.0.45)
Equation (2.0.46). Easy from hasStrongType_maximalFunction
Equations
- C_weakType_globalMaximalFunction A p₁ p₂ = ↑A ^ 2 * C_weakType_maximalFunction A p₁ p₂