Multiple vector-valued, mixed norm estimates for Littlewood-Paley square functions
We prove that for any LQ-valued Schwartz function f defined on Rd, one has the multiple vector-valued, mixed-norm estimate kfkLP (LQ) . kSfkLP (LQ) valid for every d-tuple P and every n-tuple Q satisfying 0 < P, Q < ∞ componentwise. Here S := Sd1 ⊗ · · · ⊗ SdN is a tensor product of several Li...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:264488 |
| Acceso en línea: | https://ddd.uab.cat/record/264488 https://dx.doi.org/urn:doi:10.5565/PUBLMAT6622205 |
| Access Level: | acceso abierto |
| Palabra clave: | Multi-parameter littlewood-paley theory Multi-parameter hardy spaces Mixed-norm estimates Weighted estimates and littlewood-paley theory |
| Sumario: | We prove that for any LQ-valued Schwartz function f defined on Rd, one has the multiple vector-valued, mixed-norm estimate kfkLP (LQ) . kSfkLP (LQ) valid for every d-tuple P and every n-tuple Q satisfying 0 < P, Q < ∞ componentwise. Here S := Sd1 ⊗ · · · ⊗ SdN is a tensor product of several Littlewood-Paley square functions Sdj defined on arbitrary Euclidean spaces R dj for 1 ≤ j ≤ N, with the property that d1 + · · · + dN = d. This answers a question that came up implicitly in our recent works [2], [3], [5] and completes in a natural way classical results of Littlewood-Paley theory. The proof is based on the helicoidal method introduced by the authors in the aforementioned papers. |
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