This should roughly contain the contents of chapter 9.
Lemma 9.0.2
Use the dominated convergence theorem e.g. [Folland, Real Analysis. Modern Techniques and Their Applications, Lemma 3.16]
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‖₊ ^ 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).
hasStrongType_maximalFunction
minus the assumption hR
.
A proof for basically this result is given in Chapter 9, everything following after equation
(9.0.36).
Use lowerSemicontinuous_iff_isOpen_preimage
and continuous_average_ball
hasStrongType_maximalFunction
minus the assumption hR
, but where p₁ = p₂
is possible and
we only conclude a weak-type estimate.
The proof of this should be basically the same as that of hasStrongType_maximalFunction
+
hasStrongType_maximalFunction_todo
, but starting with HasWeakType.MB_one
instead of
hasStrongType_MB
. (For p₂ > p₁
you can also derive this from
hasStrongType_maximalFunction_todo
)
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
. Ideally prove separately
HasStrongType.const_smul
and HasStrongType.const_mul
.