Hörmander type theorem for multilinear pseudo-differential operators

被引:0
|
作者
Heo, Yaryong [1 ]
Hong, Sunggeum [2 ]
Yang, Chan Woo [1 ]
机构
[1] Korea Univ, Dept Math, Seoul 02841, South Korea
[2] Chosun Univ, Dept Math, Gwangju 61452, South Korea
基金
新加坡国家研究基金会;
关键词
Multilinear operators; H & ouml; rmander multiplier; Regular Calder & oacute; n-Zygmund kernel; Coifman-Meyer theorem; HORMANDER MULTIPLIER THEOREM; MINIMAL SOBOLEV REGULARITY; FOURIER MULTIPLIERS; LP;
D O I
10.1016/j.jmaa.2024.128903
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
We establish a H & ouml;rmander type theorem for the multilinear pseudo-differential operators, which is also a generalization of the results in [32] to symbols depending on the spatial variable. Most known results for multilinear pseudo-differential operators were obtained by assuming their symbols satisfy pointwise derivative estimates (Mihlin-type condition), that is, their symbols belong to some symbol classes n- 9' m rho,delta ( R d ), 0 < delta < rho < 1, 0 < delta < 1 for some m < 0. In this paper, we shall consider multilinear pseudo-differential operators whose symbols have limited smoothness described in terms of function space and not in a pointwise form (H & ouml;rmander type condition). Our conditions for symbols are weaker than the Mihlin-type conditions in two senses: the one is that we only assume the first-order derivative conditions in the spatial variable and lower-order derivative conditions in the frequency variable, and the other is that we make use of L 2-average condition rather than pointwise derivative conditions for the symbols. As an application, we obtain some mapping properties for the multilinear pseudo-differential operators associated with symbols belonging to the classes n- 9' rho,delta m ( R d ), 0 < rho < 1, 0 < delta < 1, m < 0. Moreover, it can be pointed out that our results are applied to wider classes of symbols which do not belong to the traditional symbol classes n- 9' m rho,delta ( R d ). (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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页数:39
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