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arXiv 2402.08474math.AP
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椭圆算子第一Robin特征值的Pólya型估计

Pólya-type estimates for the first Robin eigenvalue of elliptic operators

F. Della Pietra

AI总结:

本文研究了各向异性 $p$-Laplace 算子第一 Robin 特征值的最优估计问题,基于一维非线性问题特征值给出上界,并推导了扭转刚度的下界,结果在经典欧几里得情形下亦属新发现。

摘要:

本文旨在获得各向异性 $p$-Laplace 算子第一 Robin 特征值的最优估计,即:\\[ λ_F(β,Ω)=λ_{F}(p,β,Ω)= \min_{ψ\in W^{1,p}(Ω)\setminus\{0\} } \frac{\int_ΩF(\nabla ψ)^p dx +β\int_{\partialΩ}|ψ|^p F(ν_Ω) d\mathcal H^{N-1} }{\int_Ω|ψ|^p dx} \\] 其中 $p\in]1,+\infty[$,$Ω$ 是 $\mathbb R^{N}$ 中的有界凸区域,$ν_Ω$ 是其欧几里得外法向量,$β$ 是实数,$F$ 是 $\mathbb R^{N}$ 上充分光滑的范数。我们展示了 $λ_{F}(β,Ω)$ 的上界,用一维非线性问题的第一特征值表示,该问题依赖于 $β$ 以及 $Ω$ 的体积和各向异性周长,秉承 Pólya 对欧几里得 Dirichlet Laplacian 的经典估计精神。我们还将给出扭转刚度的下界 \\[ τ_p(β,Ω)^{p-1} = \max_{\substack{ψ\in W^{1,p}(Ω)\setminus\{0\}}} \dfrac{\left(\int_Ω|ψ| \\, dx\right)^p}{\int_ΩF(\nablaψ)^p dx+β\int_{\partialΩ}|ψ|^p F(ν_Ω) d\mathcal H^{N-1} }, \\] 当 $β>0$ 时。所得结果在经典欧几里得 Laplacian 情形下也是新的。

英文摘要:

The aim of this paper is to obtain optimal estimates for the first Robin eigenvalue of the anisotropic $p$-Laplace operator, namely: \[ λ_F(β,Ω)=λ_{F}(p,β,Ω)= \min_{ψ\in W^{1,p}(Ω)\setminus\{0\} } \frac{\int_ΩF(\nabla ψ)^p dx +β\int_{\partialΩ}|ψ|^p F(ν_Ω) d\mathcal H^{N-1} }{\int_Ω|ψ|^p dx} \] where $p\in]1,+\infty[$, $Ω$ is a bounded, convex domain in $\mathbb R^{N}$, $ν_Ω$ is its Euclidean outward normal, $β$ is a real number, and $F$ is a sufficiently smooth norm on $\mathbb R^{N}$. We show an upper bound for $λ_{F}(β,Ω)$ in terms of the first eigenvalue of a one-dimensional nonlinear problem, which depends on $β$ and on the volume and the anisotropic perimeter of $Ω$, in the spirit of the classical estimates of Pólya \cite{po61} for the Euclidean Dirichlet Laplacian. We will also provide a lower bound for the torsional rigidity \[ τ_p(β,Ω)^{p-1} = \max_{\substack{ψ\in W^{1,p}(Ω)\setminus\{0\}}} \dfrac{\left(\int_Ω|ψ| \, dx\right)^p}{\int_ΩF(\nablaψ)^p dx+β\int_{\partialΩ}|ψ|^p F(ν_Ω) d\mathcal H^{N-1} }, \] when $β>0$. The obtained results are new also in the case of the classical Euclidean Laplacian.

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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