Search arXivSearch

arXiv · 2410.15791

Smoothing inequalities for corner-type bilinear averages: geometric characterization and applications

Abstract

We study Sobolev smoothing inequalities for bilinear averages associated with corner-type configurations \[ A_{γ,ρ}(f_1,f_2)(x_1,x_2) =\int f_1(x_1+γ_1(t),x_2)f_2(x_1,x_2+γ_2(t))ρ(t)\,dt. \] In the real-analytic setting, we obtain a complete geometric characterization of the curves $γ=(γ_{1},γ_{2})$ for which $A_{γ,ρ}(f_1,f_2)$ satisfies the smoothing inequality \[ \|A_{γ,ρ}(f_1,f_2)\|_{L^1} \lesssim \left\|f_1 \right\|_{H^{(-\varepsilon,0)}} \cdot \left\|f_2 \right\|_{H^{(0,-\varepsilon)}}\, \] for some $\varepsilon >0$. For general $C^4 $ embedded curves, we establish analogous quantitative statement involving purely geometric conditions that encode certain uniform complexity bounds and allows degeneracies on the various curvature conditions. For definable families of curves in an arbitrary o-minimal expansion of the real field, the relevant complexity parameters are uniformly finite, leading to smoothing inequalities that hold uniformly across the family. As applications, we obtain bounds for triangular Hilbert transforms along a large family of curves, their associated maximal operators, and corner-type lacunary spherical maximal operators. We further prove the existence of configurations of the form $(x,y)$, $(x+γ_{1}(t),y)$, $(x,y+γ_{2}(t))$ inside sets of positive measure, together with a quantitative lower bound on the gap $t$.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Martin Hsu, Fred Yu-Hsiang Lin. 2026-07-18. Smoothing inequalities for corner-type bilinear averages: geometric characterization and applications. https://arxiv.org/abs/2410.15791

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Fragment-wise differentiable structures

The $p$-modulus of curves, test plans, upper gradients, charts, differentials, approximations in energy and density of directions are all concepts associated to the theory of Sobolev functions in metric measure spaces. The purpose of this paper is to give an analogous geometric and ``fragment-wise'' theory for Lipschitz functions and Weaver derivations, where $\infty$-modulus of curve fragments, $\ast$-upper gradients and Alberti representations play a central role. We give a new definition of fragment-wise charts and prove that they exists for spaces with finite Hausdorff dimension. We give a replacement for $p$-duality in terms of Alberti representations and $\infty$-modulus and present the theory of $\ast$-upper gradients. Further, we give new and sharper results for approximations of Lipschitz functions, which yields the density of directions. Our results are applicable to all complete and separable metric measure spaces. In the process, we show that there are strong parallels between the Sobolev and Lipschitz worlds.

math.CA

Tensor Derivatives, Unified Tensor-Form Differential Equations, and Model Reduction via Partial Tucker Decomposition

This paper develops a unified tensor calculus for matrix-valued functions and their derivatives, and leverages this framework to construct efficient model reduction techniques for high-dimensional tensor differential equations. We first establish a systematic theory of tensor differentiation, wherein the derivative of a matrix with respect to another matrix is represented as a fourth-order tensor. Building on this calculus, we recast linear ordinary differential equations (ODEs) and partial differential equations(PDEs) into a compact tensor-matrix form $\frac{dX}{dt} = \A\ast X$. The general solution is expressed as $X = \exp(t\A)\ast C$, extending the matrix exponential to the tensor setting. Conditions under which the solution admits this exponential form are characterized in terms of the commutativity of the associated matrix slices. We introduce the partial Tucker decomposition (parTuckerD) to address the computational challenges posed by high-order tensor systems. On a synthetic electronic health record (EHR) tensor, parTuckerD achieves a relative reconstruction error of $0.0992$ with a $136.3\times$ compression ratio, matching the accuracy of the full TuckerD while preserving patient-level similarity structure. The results demonstrate that the proposed tensor calculus and parTuckerD framework provide a principle and computationally efficient approach for analyzing and solving high-dimensional tensor differential equations arising in data-intensive applications.

math.CA

Distance preservers for Lobachevsky space

We obtain a complete description of the class of entrywise preservers of Lorentz-Gram matrices. This resolves, for the case of constant negative curvature, the classification of entrywise preservers obtained by Schoenberg in the zero-curvature (Euclidean) and constant-positive-curvature (spherical) settings. These preservers admit a Lévy--Khintchine-type representation and their asymptotic characteristics are related to Krein's classification of screw lines in Lobachevsky space. Connections with complete Nevanlinna--Pick kernels and Bochner subordination are also obtained.

math.CA