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This work is on constrained large-scale non-convex optimization where the constraint set implies a manifold structure. Solving such problems is important in a multitude of fundamental machine learning tasks. Recent advances on Riemannian…

Machine Learning · Computer Science 2023-02-23 Yian Deng , Tingting Mu

In this work we introduce a formulation for a non-local Hessian that combines the ideas of higher-order and non-local regularization for image restoration, extending the idea of non-local gradients to higher-order derivatives. By carefully…

Numerical Analysis · Mathematics 2014-11-03 Jan Lellmann , Konstantinos Papafitsoros , Carola Schoenlieb , Daniel Spector

In probabilistic modeling, parameter estimation is commonly formulated as a minimization problem on a parameter manifold. Optimization in such spaces requires geometry-aware methods that respect the underlying information structure. While…

Computation · Statistics 2025-11-17 Derun Zhou , Keisuke Yano , Mahito Sugiyama

This paper presents a perturbation analysis framework for nonsmooth optimization on connected Riemannian manifolds to bridge the gap between the rapid development of algorithmic approaches and a robust theoretical foundation. Using…

Optimization and Control · Mathematics 2025-10-01 Yuexin Zhou , Chao Ding , Yangjing Zhang

In this work we analyse the convergence of solutions of the Poisson equation with Neumann boundary conditions in a thin domain with highly oscillatory behavior $\mathcal{U}^\varepsilon$ contained in the sphere $\mathbb{S}^2$. Using the…

Analysis of PDEs · Mathematics 2026-03-04 Naísa C. Garcia , Raquel Lehrer , Marcus A. M. Marrocos

Numerical solution of the Poisson equation in metallic enclosures, open at one or more ends, is important in many practical situations such as High Power Microwave (HPM) or photo-cathode devices. It requires imposition of a suitable…

Computational Physics · Physics 2015-10-16 Debabrata Biswas , Gaurav Singh , Raghwendra Kumar

In this paper, we look at a probabilistic approach to a non-local quadratic form that has lately attracted some interest. This form is related to a recently introduced non-local normal derivative. The goal is to construct two Markov…

Probability · Mathematics 2019-09-25 Zoran Vondraček

We derived here in a systematic way, and for a large class of scaling regimes, asymptotic models for the propagation of internal waves at the interface between two layers of immiscible fluids of different densities, under the rigid lid…

Analysis of PDEs · Mathematics 2007-12-27 Jerry L. Bona , David Lannes , Jean-Claude Saut

We introduce a new Neumann problem for the fractional Laplacian arising from a simple probabilistic consideration, and we discuss the basic properties of this model. We can consider both elliptic and parabolic equations in any domain. In…

Analysis of PDEs · Mathematics 2014-11-03 Serena Dipierro , Xavier Ros-Oton , Enrico Valdinoci

In this paper we study nonlinear second-order differential inclusions involving the ordinary vector $p$-Laplacian, a multivalued maximal monotone operator and nonlinear multivalued boundary conditions. Our framework is general and unifying…

Classical Analysis and ODEs · Mathematics 2007-05-23 Leszek Gasinski , Nikolaos S. Papageorgiou

We construct a first order local model for Poisson manifolds around a large class of Poisson submanifolds and we give conditions under which this model is a local normal form. The resulting linearization theorem includes as special cases…

Symplectic Geometry · Mathematics 2023-07-18 Rui Loja Fernandes , Ioan Marcut

Contrary to standard statistical models, unnormalised statistical models only specify the likelihood function up to a constant. While such models are natural and popular, the lack of normalisation makes inference much more difficult. Here…

Computation · Statistics 2014-12-01 Simon Barthelmé , Nicolas Chopin

We give a necessary and sufficient condition for a 2-dimensional Riemannian manifold to be locally isometrically immersed into a 3-dimensional homogeneous manifold with a 4-dimensional isometry group. The condition is expressed in terms of…

Differential Geometry · Mathematics 2010-03-25 Benoit Daniel

We derive a priori second order estimates for solutions of a class of fully nonlinear elliptic equations on Riemannian manifolds under some very general structure conditions. We treat both equations on closed manifolds, and the Dirichlet…

Analysis of PDEs · Mathematics 2015-01-14 Bo Guan

We develop a rigorous theoretical framework for principal manifold estimation that recovers a latent low-dimensional manifold from a point cloud observed in a high-dimensional ambient space. Our framework accommodates manifolds with…

Statistics Theory · Mathematics 2026-04-07 Kun Meng , Christopher Perez

In this article, we propose a MUSCL-Hancock-type second-order scheme for the discretization of a general class of non-local conservation laws and present its convergence analysis. The main difficulty in designing a MUSCL-Hancock-type scheme…

Numerical Analysis · Mathematics 2025-06-05 Nikhil Manoj , G. D. Veerappa Gowda , Sudarshan Kumar K

A nonlocal contact problem for two-dimensional linear elliptic equations is stated and investigated. The method of separation of variables is used to find the solution of a stated problem in case of Poisson's equation. Then the more general…

Analysis of PDEs · Mathematics 2024-05-29 Tinatin Davitashvili , Hamlet Meladze , Francisco Criado-Aldeanueva , Jose Maria Sanchez

Cellular electrophysiology is often modeled using the cable equations. The cable model can only be used when ionic concentration effects and three dimensional geometry effects are negligible. The Poisson model, in which the electrostatic…

Neurons and Cognition · Quantitative Biology 2009-01-27 Yoichiro Mori

We establish strong dynamical localization for a class of multi-particle Anderson models in a Euclidean space with an alloy-type random potential and a sub-exponentially decaying interaction of infinite range. For the first time in the…

Mathematical Physics · Physics 2014-07-18 Victor Chulaevsky

We design an algorithm which finds an $\epsilon$-approximate stationary point (with $\|\nabla F(x)\|\le \epsilon$) using $O(\epsilon^{-3})$ stochastic gradient and Hessian-vector products, matching guarantees that were previously available…

Machine Learning · Computer Science 2020-06-25 Yossi Arjevani , Yair Carmon , John C. Duchi , Dylan J. Foster , Ayush Sekhari , Karthik Sridharan