The height function of a sparse collection: a Bellman function approach

Sparse operators have emerged as a powerful method to extract sharp constants in harmonic analysis inequalities, for example in the context of bounding singular integral operators. We investigate the level sets of height functions for sparse collections, or, in other words, weak-type (1,1) inequalities for sparse operators applied to constant functions. We use another notable method from dyadic harmonic analysis, also famous for its ability to produce sharp constants, the Bellman function method. Specifically, we find the exact Bellman function maximizing level sets of $\mathcal{A}_α1\!\!1$, where $\mathcal{A}_α$ is the (localized) sparse operator associated with a binary Carleson sequence.

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ArXiv.org
Published
2025-09-30
Primary Topic
Face and Expression Recognition
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article
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article

The height function of a sparse collection: a Bellman function approach

ArXiv.org
Face and Expression Recognition
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The height function of a sparse collection: a Bellman function approach

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Abstract

Sparse operators have emerged as a powerful method to extract sharp constants in harmonic analysis inequalities, for example in the context of bounding singular integral operators. We investigate the level sets of height functions for sparse collections, or, in other words, weak-type (1,1) inequalities for sparse operators applied to constant functions. We use another notable method from dyadic harmonic analysis, also famous for its ability to produce sharp constants, the Bellman function method. Specifically, we find the exact Bellman function maximizing level sets of $\mathcal{A}_α1\!\!1$, where $\mathcal{A}_α$ is the (localized) sparse operator associated with a binary Carleson sequence.

ArXiv.org
Reduced inequalities
Openalex Percentile: Top 44%
Face and Expression Recognition
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The height function of a sparse collection: a Bellman function approach · ArXiv.org (2025) | TGRS Research Map | TGRS