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Related papers: Kurdyka-{\L}ojasiewicz exponent via Hadamard param…

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For a given second order elliptic operation $\mathcal{L}$ in a domain $\Omega\subset{\mathbb{R}}^\mathbf{N}$, $\mathbf{N}\ $, and a compact set $\mathbf{K}\subset\Omega$, order $\mathbf{N}$-$2$-Ahlfors-David regular, we define the space…

Analysis of PDEs · Mathematics 2026-01-07 Grigori Rozenblum , Nikolay Shirokov

We develop a unifying framework for interpolatory $\mathcal{L}_2$-optimal reduced-order modeling for a wide classes of problems ranging from stationary models to parametric dynamical systems. We first show that the framework naturally…

Numerical Analysis · Mathematics 2023-09-26 Petar Mlinarić , Serkan Gugercin

We consider the problems of computing the optimal rank-$1$ Hankel and Toeplitz-structured approximation of arbitrary matrices under $L_2$ and $L_1$-norm error. Such problems arise naturally in engineered systems, including the basic…

Machine Learning · Computer Science 2026-05-07 Georgios I. Orfanidis

We recall the notions of Fr\"olicher and diffeological spaces and we build regular Fr\"olicher Lie groups and Lie algebras of formal pseudo-differential operators in one independent variable. Combining these constructions with a smooth…

Mathematical Physics · Physics 2020-02-04 Jean-Pierre Magnot , Enrique G. Reyes

Extended dynamic mode decomposition (EDMD) is a well-established method to generate a data-driven approximation of the Koopman operator for analysis and prediction of nonlinear dynamical systems. Recently, kernel EDMD (kEDMD) has gained…

Dynamical Systems · Mathematics 2024-07-08 Frederik Köhne , Friedrich M. Philipp , Manuel Schaller , Anton Schiela , Karl Worthmann

Several widely-used first-order saddle-point optimization methods yield an identical continuous-time ordinary differential equation (ODE) that is identical to that of the Gradient Descent Ascent (GDA) method when derived naively. However,…

Optimization and Control · Mathematics 2023-08-01 Tatjana Chavdarova , Michael I. Jordan , Manolis Zampetakis

We prove sharp L^2 boundary decay estimates for the eigenfunctions of certain second order elliptic operators acting in a bounded region, and of their first order space derivatives, using only the Hardy inequality. We then deduce bounds on…

Spectral Theory · Mathematics 2007-05-23 E B Davies

In this paper, we investigate the growth error bound condition. By using the proximal point algorithm, we first provide a more accessible and elementary proof of the fact that Kurdyka-{\L}ojasiewicz conditions imply growth error bound…

Optimization and Control · Mathematics 2024-06-12 Qinian Jin

An efficient $hp$-multigrid scheme is presented for local discontinuous Galerkin (LDG) discretizations of elliptic problems, formulated around the idea of separately coarsening the underlying discrete gradient and divergence operators. We…

Numerical Analysis · Mathematics 2019-03-14 Daniel Fortunato , Chris H. Rycroft , Robert Saye

This paper investigates the local regularity of solutions to stationary Fokker-Planck equations on an open set $U \subset \mathbb{R}^d$ with $d \geq 2$. A central objective is to relax the classical assumptions on the coefficients by…

Analysis of PDEs · Mathematics 2026-02-25 Haesung Lee

We study in this paper a multilayer discretization of second order elliptic problems, aimed at providing reliable multilayer discretizations of shallow fluid flow problems with diffusive effects. This discretization is based upon the…

Numerical Analysis · Mathematics 2018-07-17 Toms Chacón Rebollo , Daniel Franco Coronil , Frédéric Hecht

In the paper \textit{Preconditioning by inverting the {L}aplacian; an analysis of the eigenvalues. IMA Journal of Numerical Analysis 29, 1 (2009), 24--42}, Nielsen, Hackbusch and Tveito study the operator generated by using the inverse of…

Numerical Analysis · Mathematics 2018-09-12 Tomáš Gergelits , Kent-André Mardal , Bjørn Fredrik Nielsen , Zdeněk Strakoš

We provide a unifying framework for $\mathcal{L}_2$-optimal reduced-order modeling for linear time-invariant dynamical systems and stationary parametric problems. Using parameter-separable forms of the reduced-model quantities, we derive…

Numerical Analysis · Mathematics 2022-10-17 Petar Mlinarić , Serkan Gugercin

We prove $L^\infty$-error bounds for kernel extended dynamic mode decomposition (kEDMD) approximants of the Koopman operator for stochastic dynamical systems. To this end, we establish Koopman invariance of suitably chosen reproducing…

Dynamical Systems · Mathematics 2026-04-20 Maximiliano Hertel , Friedrich M. Philipp , Manuel Schaller , Karl Worthmann

In this paper, we study the Temporal Difference (TD) learning with linear value function approximation. It is well known that most TD learning algorithms are unstable with linear function approximation and off-policy learning. Recent…

Artificial Intelligence · Computer Science 2016-10-06 Dominik Meyer , Hao Shen , Klaus Diepold

An analytic theory of edge localized mode (ELM) suppression in an H-mode tokamak plasma via the application of a static, externally generated, resonant magnetic perturbation (RMP) is presented. This theory is based on the plausible…

Plasma Physics · Physics 2019-09-20 Richard Fitzpatrick

Consider the Hopf--Cole solution $ h(t,x) $ of the KPZ equation with narrow wedge initial condition. Regarding $ t\to\infty $ as a scaling parameter, we provide the first rigorous proof of the Large Deviation Principle (LDP) for the lower…

Probability · Mathematics 2018-09-11 Li-Cheng Tsai

We propose a novel iterative framework for minimizing a proper lower semicontinuous Kurdyka-{\L}ojasiewicz (KL) function $\Phi$. It comprises a Zhang-Hager (ZH-type) nonmonotone decrease condition and a relative error condition. Hence, the…

Optimization and Control · Mathematics 2024-12-05 Yitian Qian , Ting Tao , Shaohua Pan , Houduo Qi

We consider the Klein-Gordon operator on an $n$-dimensional asymptotically anti-de Sitter spacetime $(M,g)$ together with arbitrary boundary conditions encoded by a self-adjoint pseudodifferential operator on $\partial M$ of order up to…

Mathematical Physics · Physics 2021-09-01 Claudio Dappiaggi , Alessio Marta

In this paper, we study the convergence properties of the Stochastic Gradient Descent (SGD) method for finding a stationary point of a given objective function $J(\cdot)$. The objective function is not required to be convex. Rather, our…

Machine Learning · Statistics 2024-09-24 Rajeeva L. Karandikar , M. Vidyasagar