claimroutine

Kakeya maximal estimate by duality

Bounds Kakeya maximal function in LnL^n with loss L(1/δ)2A/nL(1/\delta)^{2A/n} by duality
maximal estimate behind volume and dimension claims
Claim·§9.3 The maximal estimate and Hausdorff dimension·inside corQuantitative extension and Kakeya bounds

Statement (paper)

For 0<δ≤10<\delta\le1, ∥Kδf∥Ln(Sn−1)≲nL(1/δ)2A/n∥f∥Ln(Rn)\|K_\delta f\|_{L^n(S^{n-1})}\lesssim_n L(1/\delta)^{2A/n}\|f\|_{L^n(\RR^n)} with A=2/pA=2/p. This is obtained by testing the discretized norm (δd∑jK2δf(ωj)n)1/n\big(\delta^d\sum_j K_{2\delta}f(\omega_j)^n\big)^{1/n} against weights aj≥0a_j\ge0 with δd∑jajq=1\delta^d\sum_j a_j^q=1, and bounding δd∑jaj∣Tj∣∫Tj∣f∣≲∫∣f∣∑jaj1Tj≲L(1/δ)2A∥f∥n\delta^d\sum_j \frac{a_j}{|T_j|}\int_{T_j}|f|\lesssim \int|f|\sum_j a_j\mathbf 1_{T_j}\lesssim L(1/\delta)^{2A}\|f\|_n for any choice of centers.

In plain English

For 0<δ≤10<\delta\le1, the Kakeya maximal function satisfies ∥Kδf∥Ln(Sn−1)≲nL(1/δ)2A/n∥f∥Ln(Rn)\|K_\delta f\|_{L^n(S^{n-1})}\lesssim_n L(1/\delta)^{2A/n}\|f\|_{L^n(\mathbb R^n)} with A=2/pA=2/p, so the logarithmic loss is L(1/δ)2A/nL(1/\delta)^{2A/n}.

Why it's here

The LnL^n norm of the discretized maximal function is dualized against weights aja_j in ℓq\ell^q, which converts the problem into bounding ∫∣f∣∑jaj1Tj\int|f|\sum_j a_j\mathbf 1_{T_j} for arbitrary tube centers. The overlap lemma controls this sum in LqL^q with loss L(1/δ)2AL(1/\delta)^{2A}, and Hölder's inequality then gives the claim. This maximal bound is the input for the volume and Hausdorff-dimension claims that follow.
Classified routine:
Standard duality argument (as in Bourgain 1991 and Wolff's treatment of Kakeya maximal functions) turning the tube overlap bound in L^q into a Kakeya maximal estimate on L^n via discretization and Hölder; it follows directly from the paper's overlap lemma.

Sources

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§9.3 The maximal estimate and Hausdorff dimension
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sections/kakeya.tex:182
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