A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study p-Laplacian equation on Riemannian manifolds with positive Ricci curvature.
problem Overdetermined problem for p-Laplacian equation on compact Riemannian manifolds.
method Introduced a new P-function related to the first nonzero eigenvalue for p-Laplacian, derived integral identities, and applied them to achieve inequalities and the Soap Bubble Theorem.
result Achieved the Heintze-Karcher type inequality and the Soap Bubble Theorem.
Let M be an n-dimensional closed orientable submanifold in an N-dimensional space form. When 1<p≤2n+1, we obtain an upper bound for the first nonzero eigenvalue of the p-Laplacian in terms of the mean curvature of M and the curvature of the space form. This generalizes the Reilly inequality for …
Given a Finsler manifold (M,F), it is proved that the first eigenvalue of the Finslerian p-Laplacian is bounded above by a constant depending on p, the dimension of M, the Busemann-Hausdorff volume and the reversibility constant of (M,F). For a Randers manifold (M,F:=g+β), where g is a Riemannian…
In this paper, we establish gradient estimates for positive solutions to the following equation with respect to the p-Laplacian Δpu=−λ∣u∣p−2u with p>1 on a given complete Riemannian manifold. Consequently, we derive upper bound estimates of the first nontrivial eigenvalue of the p-Laplacian.
The graph Laplacian plays key roles in information processing of relational data, and has analogies with the Laplacian in differential geometry. In this paper, we generalize the analogy between graph Laplacian and differential geometry to the hypergraph setting, and propose a novel hypergraph p-Laplacian. Unlike the …
The p-Laplacian Transformer improves transformer models by assigning higher attention weights to tokens in close proximity.
problem The self-attention mechanism in transformers does not effectively distinguish attention weights between tokens in close and non-close proximity.
method Proposes a novel class of transformers, p-Laplacian Transformers, that use p-Laplacian regularization to assign higher attention weights to tokens in close proximity.
result Empirically demonstrates that p-Laplacian Transformers outperform baseline transformers on various benchmark datasets.
We prove the sharp estimate on the first nonzero eigenvalue of the p-laplacian on a compact Riemannian manifold with nonnegative Ricci curvature and possibly with convex boundary (in this case we assume Neumann b.c. on the p-laplacian). The proof is based on a gradient comparison theorem. We will also charachterize the…
In this paper, we study eigenvalues and eigenfunctions of p-Laplacians with Dirichlet boundary condition on graphs. We characterize the first eigenfunction (and the maximum eigenfunction for a bipartite graph) via the sign condition. By the uniqueness of the first eigenfunction of p-Laplacian, as p→1, we ident…
We discuss the behavior of (λ1.p(M))1/p with respect to the Gromov-Hausdorff topology and the variable p, where λ1,p(M) is the first positive eigenvalue of the p-Laplacian on a compact Riemannian manifold M. Applications include new estimates for the first eigenvalues of the p-Laplacian on Rieman…
The paper sets up eigenvalue comparison theorems for specific Laplacians on manifolds.
problem Eigenvalue comparison theorems for Witten-Laplacian and weighted p-Laplacian on manifolds with modified Ricci curvature.
method Established Cheng-type eigenvalue comparison theorems for the first Dirichlet eigenvalues of the Witten-Laplacian and weighted p-Laplacian on geodesic balls.
result Successfully set up eigenvalue comparison theorems for the Witten-Laplacian and weighted p-Laplacian.
We prove a Lichnerowicz type lower bound for the first nontrivial eigenvalue of the p-Laplacian on Kähler manifolds. Parallel to the p=2 case, the first eigenvalue lower bound is improved by using a decomposition of the Hessian on Kähler manifolds with positive Ricci curvature.
Let M be an n-dimensional closed Riemannian manifold with metric g, dμ=e−φ(x)dν be the weighted measure and Δp,φ be the weighted p-Laplacian. In this article we will investigate monotonicity for the first eigenvalue problem of the weighted p-Laplace operator acting on the space of functions along th…
In this paper, we successfully generalize the eigenvalue comparison theorem for the Dirichlet p-Laplacian (1<p<∞) obtained by Matei [A.-M. Matei, First eigenvalue for the p-Laplace operator, Nonlinear Anal. TMA 39 (8) (2000) 1051--1068] and Takeuchi [H. Takeuchi, On the first eigenvalue of the p-Laplacian …
We prove Cheeger inequalities for p-Laplacians on finite and infinite weighted graphs. Unlike in previous works, we do not impose boundedness of the vertex degree, nor do we restrict ourselves to the normalized Laplacian and, more generally, we do not impose any boundedness assumption on the geometry. This is achieved …
Combined with our previous work \cite{LW19eigenvalue}, we prove sharp lower bound estimates for the first nonzero eigenvalue of the weighted p-Laplacian with 1<p<∞ on a compact Bakry-Émery manifold (Mn,g,f), without boundary or with a convex boundary and Neumann boundary condition, satisfying $\text{Ric}+…
We show that a Weyl law holds for the variational spectrum of the p-Laplacian. More precisely, let (λi)i=1∞ be the variational spectrum of Δp on a closed Riemannian manifold (X,g) and let N(λ)=#{i:λi<λ} be the associated counting function. Then we have a Weyl law $N(λ) \sim c \operatorna…
We complete the picture of sharp eigenvalue estimates for the p-Laplacian on a compact manifold by providing sharp estimates on the first nonzero eigenvalue of the nonlinear operator Δp when the Ricci curvature is bounded from below by a negative constant. We assume that the boundary of the manifold is convex, and p…
Most network-based machine learning methods assume that the labels of two adjacent samples in the network are likely to be the same. However, assuming the pairwise relationship between samples is not complete. The information a group of samples that shows very similar pattern and tends to have similar labels is missed.…