English

The Fractional-Logarithmic Laplacian:Fundamental Properties and Eigenvalues

Analysis of PDEs 2026-04-14 v3

Abstract

In this paper, we introduce, for the first time, the fractional--logarithmic Laplacian (Δ)s+log (-\Delta)^{s+\log} , defined as the derivative of the fractional Laplacian (Δ)t (-\Delta)^t at t=s t=s . It is a singular integral operator with Fourier symbol ξ2s(2lnξ) |\xi|^{2s}(2\ln|\xi|) , and we prove the pointwise integral representation (Δ)s+logu(x)=cn,sPV ⁣Rnu(x)u(y)xyn+2s(2lnxy)dy+bn,s(Δ)su(x), (-\Delta)^{s+\log}u(x) = c_{n,s}\,\mathrm{PV}\!\int_{\mathbb{R}^n} \frac{u(x)-u(y)}{|x-y|^{n+2s}}\bigl(-2\ln|x-y|\bigr)\,dy + b_{n,s}(-\Delta)^s u(x), where cn,s c_{n,s} is the normalization constant of the fractional Laplacian and bn,s:=ddscn,s. b_{n,s}:=\frac{d}{ds}c_{n,s}. We also establish several equivalent formulations of (Δ)s+log (-\Delta)^{s+\log} , including the singular-integral representation, the Fourier-multiplier representation, the spectral-calculus definition, and an extension characterization. We develop the associated functional framework on both Rn \mathbb{R}^n and bounded Lipschitz domains, introducing the natural energy spaces and proving embedding results. In particular, we obtain a compact embedding at the critical exponent 2s=2nn2s, 2_s^*=\frac{2n}{n-2s}, a phenomenon that differs from the classical Sobolev and fractional Sobolev settings. We further study the Poisson problem, proving existence and L L^\infty -regularity results. We then investigate the Dirichlet eigenvalue problem and establish qualitative spectral properties. Finally, we derive a Weyl-type asymptotic law for the eigenvalue counting function and for the k k -th Dirichlet eigenvalue, showing that the high-frequency behavior combines the fractional Weyl scaling with a logarithmic growth factor, thereby interpolating between the fractional Laplacian and the logarithmic Laplacian.

Keywords

Cite

@article{arxiv.2602.06581,
  title  = {The Fractional-Logarithmic Laplacian:Fundamental Properties and Eigenvalues},
  author = {Huyuan Chen and Rui Chen and Daniel Hauer},
  journal= {arXiv preprint arXiv:2602.06581},
  year   = {2026}
}
R2 v1 2026-07-01T10:24:08.863Z