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Related papers: Estimates of perturbation series for kernels

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We give sufficient conditions for a nonlocal perturbation of an integral kernel to be locally in time comparable with the kernel.

Probability · Mathematics 2013-09-12 Krzysztof Bogdan , Sebastian Sydor

We study iterations of integral kernels satisfying a transience-type condition and we prove exponential estimates analogous to Gronwall\rq{}s inequality. As a consequence we obtain estimates of Schr\"odinger perturbations of integral…

Functional Analysis · Mathematics 2012-08-17 Krzysztof Bogdan , Wolfhard Hansen , Tomasz Jakubowski

We establish sharp-in-time kernel and dispersive estimates for the Schr\"odinger equation on non-compact Riemannian symmetric spaces of any rank. Due to the particular geometry at infinity and the Kunze-Stein phenomenon, these properties…

Analysis of PDEs · Mathematics 2023-02-14 Jean-Philippe Anker , Stefano Meda , Vittoria Pierfelice , Maria Vallarino , Hong-Wei Zhang

We give upper and lower bounds of perturbation series for transition densities, corresponding to additive gradient perturbations satisfying certain space-time integrability conditions.

Probability · Mathematics 2011-10-11 Tomasz Jakubowski , Karol Szczypkowski

In this paper we develop a new framework for non-linear perturbations of the Kerr spacetime. This is based on a characterization of the Kerr spacetime in terms of a Killing spinor. On the perturbed spacetime, one can construct an…

General Relativity and Quantum Cosmology · Physics 2026-01-08 Thomas Bäckdahl

We give the upper and the lower estimates of heat kernels for Schr\"odinger operators $H=-\Delta+V$, with nonnegative and locally bounded potentials $V$ in $\mathbb{R}^d$, $d \geq 1$. We observe a factorization: the contribution of the…

Functional Analysis · Mathematics 2023-03-13 Miłosz Baraniewicz , Kamil Kaleta

Nonspherical perturbation theory has been necessary to understand the meaning of radiation in spacetimes generated through fully nonlinear numerical relativity. Recently, perturbation techniques have been found to be successful for the time…

General Relativity and Quantum Cosmology · Physics 2009-10-28 A. M. Abrahams , R. H. Price

In this paper, we focus on subspace-based learning problems, where data elements are linear subspaces instead of vectors. To handle this kind of data, Grassmann kernels were proposed to measure the space structure and used with classifiers,…

Machine Learning · Computer Science 2018-06-19 Junyuan Hong , Huanhuan Chen , Feng Lin

We present new classes of positive definite kernels on non-standard spaces that are integrally strictly positive definite or characteristic. In particular, we discuss radial kernels on separable Hilbert spaces, and introduce broad classes…

Machine Learning · Statistics 2022-06-16 Johanna Ziegel , David Ginsbourger , Lutz Dümbgen

This work develops a new method to calculate non-perturbative corrections in one-dimensional Quantum Mechanics, based on trans-series solutions to the refined holomorphic anomaly equations of topological string theory. The method can be…

High Energy Physics - Theory · Physics 2018-10-15 Santiago Codesido , Marcos Marino , Ricardo Schiappa

We provide a characterization for the continuous positive definite kernels on $\mathbb R^d$ that are invariant to linear isometries, i.e. invariant under the orthogonal group $O(d)$. Furthermore, we provide necessary and sufficient…

Statistics Theory · Mathematics 2025-06-30 Felix Benning , Max David Schölpple

In this study we consider perturbative series solution with respect to a parameter {\epsilon} > 0. In this methodology the solution is considered as an infinite sum of a series of functional terms which usually converges fast to the exact…

General Mathematics · Mathematics 2023-04-24 Markos Z. Tsoukalas , Panagiotis G. Asteris

Treating the nonlinear term of the Gross-Pitaevskii nonlinear Schrodinger equation as a perturbation of an isolated discrete eigenvalue of the linear problem one obtains a Rayleigh-Schrodinger power series. This power series is proved to be…

Mathematical Physics · Physics 2024-07-23 Andrea Sacchetti

In the framework of computational complexity and in an effort to define a more natural reduction for problems of equivalence, we investigate the recently introduced kernel reduction, a reduction that operates on each element of a pair…

Computational Complexity · Computer Science 2016-04-29 Jeffrey Finkelstein , Benjamin Hescott

The paper discusses a series of results concerning reproducing kernel Hilbert spaces, related to the factorization of their kernels. In particular, it is proved that for a large class of spaces isometric multipliers are trivial. One also…

Functional Analysis · Mathematics 2016-05-10 Rani Kumari , Jaydeb Sarkar , Srijan Sarkar , Dan Timotin

We consider the semiclassical Schr\"odinger equation on a compact negatively curved surface. For any sequence of initial data microlocalized on the unit cotangent bundle, we look at the quantum evolution (below the Ehrenfest time) under…

Analysis of PDEs · Mathematics 2015-02-03 Suresh Eswarathasan , Gabriel Riviere

Kernel theorems, in general, provide a convenient representation of bounded linear operators. For the operator acting on a concrete function space, this means that its action on any element of the space can be expressed as a generalised…

Functional Analysis · Mathematics 2024-05-22 Dimitri Bytchenkoff , Michael Speckbacher , Peter Balazs

For the last one and a half decades it has been known that the exponential product formula holds also {\it in norm} in nontrivial cases. In this note, we review the results on its convergence in norm as well as pointwise of the integral…

Mathematical Physics · Physics 2009-05-22 Takashi Ichinose , Hideo Tamura

Time series are an interesting frontier for kernel-based methods, for the simple reason that there is no kernel designed to represent them and their unique characteristics in full generality. Existing sequential kernels ignore the time…

Machine Learning · Statistics 2020-04-21 Ahmed Guecioueur , Franz J. Király

This work provides theoretical foundations for kernel methods in the hyperspherical context. Specifically, we characterise the native spaces (reproducing kernel Hilbert spaces) and the Sobolev spaces associated with kernels defined over…

Machine Learning · Statistics 2022-11-18 Simon Hubbert , Emilio Porcu , Chris. J. Oates , Mark Girolami
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