ﻻ يوجد ملخص باللغة العربية
We prove a local Faber-Krahn inequality for solutions $u$ to the Dirichlet problem for $Delta + V$ on an arbitrary domain $Omega$ in $mathbb{R}^n$. Suppose a solution $u$ assumes a global maximum at some point $x_0 in Omega$ and $u(x_0)>0$. Let $T(x_0)$ be the smallest time at which a Brownian motion, started at $x_0$, has exited the domain $Omega$ with probability $ge 1/2$. For nice (e.g., convex) domains, $T(x_0) asymp d(x_0,partialOmega)^2$ but we make no assumption on the geometry of the domain. Our main result is that there exists a ball $B$ of radius $asymp T(x_0)^{1/2}$ such that $$ | V |_{L^{frac{n}{2}, 1}(Omega cap B)} ge c_n > 0, $$ provided that $n ge 3$. In the case $n = 2$, the above estimate fails and we obtain a substitute result. The Laplacian may be replaced by a uniformly elliptic operator in divergence form. This result both unifies and strenghtens a series of earlier results.
In this paper we prove a reverse Faber-Krahn inequality for the principal eigenvalue $mu_1(Omega)$ of the fully nonlinear eigenvalue problem [ label{eq} left{begin{array}{r c l l} -lambda_N(D^2 u) & = & mu u & text{in }Omega, u & = & 0 & text{on }pa
We study a Rayleigh-Faber-Krahn inequality for regional fractional Laplacian operators. In particular, we show that there exists a compactly supported nonnegative Sobolev function $u_0$ that attains the infimum (which will be a positive real number)
For a domain $Omega subset mathbb{R}^n$ and a small number $frak{T} > 0$, let [ mathcal{E}_0(Omega) = lambda_1(Omega) + {frak{T}} {text{tor}}(Omega) = inf_{u, w in H^1_0(Omega)setminus {0}} frac{int | abla u|^2}{int u^2} + {frak{T}} int frac{1}{2
The objective of this paper is two-fold. First, we establish new sharp quantitative estimates for Faber-Krahn inequalities on simply connected space forms. In these spaces, geodesic balls uniquely minimize the first eigenvalue of the Dirichlet Laplac
A new technique was recently developed to approximate the solution of the Schroedinger equation. This approximation (dubbed ERS) is shown to yield a better accuracy than the WKB-approximation. Here, we review the ERS approximation and its application