claimroutineinferred_dep
Random signs give weak-norm lower bound
Random signs give and weak norm
combines the geometry into the weak-norm lower bound
Claim·§C Disjoint packets and the weak spacetime norm·inside propWeak-type endpoint smoothing fails logarithmically
Statement (paper)
There is a choice of signs such that, with and , one has , , , and consequently for .
In plain English
Signs can be chosen so that has , Fourier support in , and . Consequently, for , .
Why it's here
This claim combines the two geometric facts: the packets start disjoint, which keeps the norm of bounded, and their output cores all land in the small set with amplitude about 1. Random signs (Khintchine-type averaging) make the sum on have square-function size, giving an lower bound of order 1 on a set of measure . Hölder then converts this into the weak lower bound , and since this gives the parent proposition's lower bound.
Classified routine:
Combines the two preceding claims with Khintchine's inequality over random signs (averaging in expectation) and Hölder's inequality on a set of small measure to convert an L^2 lower bound into a weak-L^p lower bound. This is the standard randomization argument from Kakeya/Besicovitch-type counterexamples.
Uses
Used by
Nothing in the paper
Sources
PaperOpen
- Section
- §C Disjoint packets and the weak spacetime norm
- Page
- —
- Label
—- Line
sections/weak.tex:102
Leannot aligned
Phase 2 aligns this node to a Lean declaration.
Friction
- infoinferred_depoutline: "Random signs give weak-norm lower bound" rests on claim-prop-weak-obstruction-1
- infoinferred_depoutline: "Random signs give weak-norm lower bound" rests on claim-prop-weak-obstruction-2