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arXiv 1808.01985math.CAmath.FA
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多线性权重类的定量估计与外推

Quantitative estimates and extrapolation for multilinear weight classes

Zoe Nieraeth

AI总结:

该研究提出定量多线性有限范围外推定理,拓展已有外推结果,发展适配多子线性哈代-李特尔伍德极大算子的多线性Rubio de Francia算法,得到多类空间相关估计及推论。

摘要:

本文证明了一个定量多线性有限范围外推定理,该定理允许我们从包含部分指数为无穷大情况的加权估计出发进行外推,拓展了Li、Martell和Ombrosi近期的外推结果。我们还得到了包括ℓ^∞空间在内的向量值估计,尤其能够重新证明通过Benea和Muscalu的螺旋法得到的双线性希尔伯特变换的所有向量值界。此外,我们的结果是定量的,特别地可将巴拿赫空间框架下由稀疏控制得到的定量估计推广到拟巴拿赫空间框架。我们的证明不依赖任何非对角外推结果,还发展了适配多子线性哈代-李特尔伍德极大算子的Rubio de Francia算法的多线性版本。作为推论,我们得到了弱型空间和BMO空间中部分上下端点估计的多线性外推结果。

英文摘要:

In this paper we prove a quantitative multilinear limited range extrapolation theorem which allows us to extrapolate from weighted estimates that include the cases where some of the exponents are infinite. This extends the recent extrapolation result of Li, Martell, and Ombrosi. We also obtain vector-valued estimates including $\ell^\infty$ spaces and, in particular, we are able to reprove all the vector-valued bounds for the bilinear Hilbert transform obtained through the helicoidal method of Benea and Muscalu. Moreover, our result is quantitative and, in particular, allows us to extend quantitative estimates obtained from sparse domination in the Banach space setting to the quasi-Banach space setting. Our proof does not rely on any off-diagonal extrapolation results and we develop a multilinear version of the Rubio de Francia algorithm adapted to the multisublinear Hardy-Littlewood maximal operator. As a corollary, we obtain multilinear extrapolation results for some upper and lower endpoints estimates in weak-type and BMO spaces.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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