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arXiv 1402.3886math.CA
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矩阵$A_2$权的希尔伯特变换的界

Bounds for the Hilbert Transform with Matrix $A_2$ Weights

Kelly Bickel, Stefanie Petermichl, Brett Wick

摘要:

令$W$表示矩阵$A_2$权重。在本文中,我们实现了一个标量参数使用平方函数推导平方函数型结果向量值的功能在$L^2(\mathbb{R},\mathbb{C}^d)$。然后利用这些结果研究了希尔伯特变换和Haar乘子在$L^2(\mathbb{R},\mathbb{C}^d)$上的有界性.我们的证明缩短了Treil和Volberg的原始参数,并提高了对$A_2$特征的依赖性。特别是,我们证明了希尔伯特变换和Haar乘子映射$L^2(\mathbb{R},W,\mathbb{C}^d)$到其自身与$A_2$特性的依赖最多$[W]_{A_2}^{\frac{3}{2}} \log [W]_{A_2}$。

英文摘要:

Let $W$ denote a matrix $A_2$ weight. In this paper, we implement a scalar argument using the square function to deduce square-function type results for vector-valued functions in $L^2(\mathbb{R},\mathbb{C}^d)$. These results are then used to study the boundedness of the Hilbert transform and Haar multipliers on $L^2(\mathbb{R},\mathbb{C}^d)$. Our proof shortens the original argument by Treil and Volberg and improves the dependence on the $A_2$ characteristic. In particular, we prove that the Hilbert transform and Haar multipliers map $L^2(\mathbb{R},W,\mathbb{C}^d)$ to itself with dependence on on the $A_2$ characteristic at most $[W]_{A_2}^{\frac{3}{2}} \log [W]_{A_2}$.

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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