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Related papers: Quadrature domains and kernel function zipping

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Let $D$ be a pseudoconvex domain in $\C^k_t\times\Cn_z$ and let $\phi$ be a plurisubharmonic function in $D$. For each $t$ we consider the $n$-dimensional slice of $D$, $D_t=\{z; (t,z)\in D\}$, let $\phi^t$ be the restriction of $\phi$ to…

Complex Variables · Mathematics 2007-05-23 Bo Berndtsson

In this paper we propose a family of tractable kernels that is dense in the family of bounded positive semi-definite functions (i.e. can approximate any bounded kernel with arbitrary precision). We start by discussing the case of stationary…

Machine Learning · Statistics 2015-10-13 Yves-Laurent Kom Samo , Stephen Roberts

We consider a class of domains, generalizing the upper half-plane, and admitting rotational, translational and scaling symmetries, analogous to the half-plane. We prove Paley-Wiener type representations of functions in Bergman spaces of…

Complex Variables · Mathematics 2018-07-03 Debraj Chakrabarti , Pranav Upadrashta

Given a smooth simply connected planar domain, the area is bounded away from zero in terms of the maximal curvature alone. We show that in higher dimensions this is not true, and for a given maximal mean curvature we provide smooth…

Optimization and Control · Mathematics 2016-04-21 Vincenzo Ferone , Carlo Nitsch , Cristina Trombetti

Kernel mean embeddings -- integrals of a kernel with respect to a probability distribution -- are essential in Bayesian quadrature, but also widely used in other computational tools for numerical integration or for statistical inference…

Machine Learning · Statistics 2025-04-29 François-Xavier Briol , Alexandra Gessner , Toni Karvonen , Maren Mahsereci

We study properties of weighted Szeg\H{o} and Garabedian kernels on planar domains. Motivated by the unweighted case as explained in Bell's work, the starting point is a weighted Kerzman-Stein formula that yields boundary smoothness of the…

Complex Variables · Mathematics 2025-06-19 Aakanksha Jain , Kaushal Verma

Classically domain theory is a rigourous mathematical structure to describe denotational semantics for programming languages and to study the computability of partial functions. Recently, the application of domain theory has also been…

Quantum Physics · Physics 2007-05-23 Elham Kashefi

We prove that a harmonic quasiconformal mapping defined on a finitely connected domain in the plane, all of whose boundary components are either points or quasicircles, admits a quasiconformal extension to the whole plane if its Schwarzian…

Complex Variables · Mathematics 2021-02-05 Iason Efraimidis

We consider polynomial Bergman kernels with respect to exponentially varying weights $e^{-n \mathscr Q(z)}$ depending on a potential $\mathscr Q:\mathbb C^d\to\mathbb R$. We use these kernels to construct determinantal point processes on…

Probability · Mathematics 2026-05-19 L. D. Molag

The theory of analytic function spaces in very general tubular domains over symmetric cones is a relatively new interesting research area. Tube domains are very general and very complicated domains. Recently several new results in this…

Complex Variables · Mathematics 2025-09-29 R. F. Shamoyan

We construct domain wall fermions with a staggered kernel and investigate their spectral and chiral properties numerically in the Schwinger model. In some relevant cases we see an improvement of chirality by more than an order of magnitude…

High Energy Physics - Lattice · Physics 2016-12-21 Christian Hoelbling , Christian Zielinski

Given a planar domain $\Omega$, the Bergman analytic content measures the $L^{2}(\Omega)$-distance between $\bar{z}$ and the Bergman space $A^{2}(\Omega)$. We compute the Bergman analytic content of simply-connected quadrature domains with…

Complex Variables · Mathematics 2016-02-12 Matthew Fleeman , Erik Lundberg

The structural properties of packed soft-core particles provide a platform to understand the cross-pollinated physical concepts in solid-state- and soft-matter physics. Confined on spherical surface, the traditional differential geometry…

Soft Condensed Matter · Physics 2025-01-06 Han Xie , Wenyu Liu , Zhenyue Lu , Jeff Z. Y. Chen , Yao Li

We study classes of reproducing kernels $K$ on general domains; these are kernels which arise commonly in machine learning models; models based on certain families of reproducing kernel Hilbert spaces. They are the positive definite kernels…

Functional Analysis · Mathematics 2017-08-22 Palle Jorgensen , Feng Tian

We derive optimal estimates for the Bergman kernel and the Bergman metric for certain model domains in $\mathbb{C}^2$ near boundary points that are of infinite type. Being unbounded models, these domains obey certain geometric constraints…

Complex Variables · Mathematics 2021-03-25 Gautam Bharali

Quantum bits can be isolated to perform useful information-theoretic tasks, even though physical systems are fundamentally described by very high-dimensional operator algebras. This is because qubits can be consistently embedded into…

Quantum Physics · Physics 2023-10-04 Andrew J. P. Garner , Markus P. Mueller

We solved the problem of the best rational approximation of the Bergman kernels on the unit circle of the complex plane in the quadratic and uniform metrics.

Complex Variables · Mathematics 2017-11-16 Stanislav Chaichenko

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 introduce two classes of "egg type" domains, built on general bounded symmetric domains, for which we compute the Bergmann kernel in explicit form. We use the characterization of bounded symmetric domains through Jordan triple systems.…

Complex Variables · Mathematics 2007-05-23 Guy Roos , Weiping Yin

We consider the problem of improving kernel approximation via randomized feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions and scale up kernel methods for larger datasets. Based on…

Machine Learning · Computer Science 2018-10-31 Marina Munkhoeva , Yermek Kapushev , Evgeny Burnaev , Ivan Oseledets
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