bergman kernels
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2020 ◽  
Vol 9 (3) ◽  
pp. 665-679
Author(s):  
L. F. Reséndis O. ◽  
L. M. Tovar S.

Abstract In this article, we define the bicomplex weighted Bergman spaces on the bidisk and their associated weighted Bergman projections, where the respective Bergman kernels are determined. We study also the bicomplex Bergman projection onto the bicomplex Bloch space.


2020 ◽  
Vol 306 (1) ◽  
pp. 67-93
Author(s):  
Debraj Chakrabarti ◽  
Austin Konkel ◽  
Meera Mainkar ◽  
Evan Miller

Axioms ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 48
Author(s):  
Elisabetta Barletta ◽  
Sorin Dragomir ◽  
Francesco Esposito

We review several results in the theory of weighted Bergman kernels. Weighted Bergman kernels generalize ordinary Bergman kernels of domains Ω ⊂ C n but also appear locally in the attempt to quantize classical states of mechanical systems whose classical phase space is a complex manifold, and turn out to be an efficient computational tool that is useful for the calculation of transition probability amplitudes from a classical state (identified to a coherent state) to another. We review the weighted version (for weights of the form γ = | φ | m on strictly pseudoconvex domains Ω = { φ < 0 } ⊂ C n ) of Fefferman’s asymptotic expansion of the Bergman kernel and discuss its possible extensions (to more general classes of weights) and implications, e.g., such as related to the construction and use of Fefferman’s metric (a Lorentzian metric on ∂ Ω × S 1 ). Several open problems are indicated throughout the survey.


Author(s):  
Hugues Auvray ◽  
Xiaonan Ma ◽  
George Marinescu

2020 ◽  
Vol 373 (3) ◽  
pp. 1049-1083 ◽  
Author(s):  
Haakan Hedenmalm ◽  
Aron Wennman

2019 ◽  
Vol 18 (01) ◽  
pp. 149-183 ◽  
Author(s):  
Saverio Salzo ◽  
Johan A. K. Suykens

In this paper, we study the variational problem associated to support vector regression in Banach function spaces. Using the Fenchel–Rockafellar duality theory, we give an explicit formulation of the dual problem as well as of the related optimality conditions. Moreover, we provide a new computational framework for solving the problem which relies on a tensor-kernel representation. This analysis overcomes the typical difficulties connected to learning in Banach spaces. We finally present a large class of tensor-kernels to which our theory fully applies: power series tensor kernels. This type of kernels describes Banach spaces of analytic functions and includes generalizations of the exponential and polynomial kernels as well as, in the complex case, generalizations of the Szegö and Bergman kernels.


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