Results 11 to 20 of about 161,671 (322)
The Kernel of a Block of a Group Algebra [PDF]
Proceedings of the American Mathematical Society, 1973 Avoiding the theory of characters of finite groups and group algebras over fields of characteristic zero a ring theoretical proof is given for R. Brauer’s theorem which asserts that the (modular) kernel of a block of a group algebra FG of a finite group over a field F of characteristic p > 0 p > 0 is a p ...
Gerhard O. Michler
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Invertibility in the flag kernels algebra on the Heisenberg group [PDF]
Journal of Fourier Analysis and Applications, 2015 Flag kernels are tempered distributions which generalize these of Calderon-Zygmund type. For any homogeneous group $\mathbb{G}$ the class of operators which acts on $L^{2}(\mathbb{G})$ by convolution with a flag kernel is closed under composition. In the
Kępa, Grzegorz
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Algebraicity of the Bergman Kernel
Mathematische Annalen, 2020 Our main result introduces a new way to characterize two-dimensional finite ball quotients by algebraicity of their Bergman kernels. This characterization is particular to dimension two and fails in higher dimensions, as is illustrated by a counterexample in dimension three constructed in this paper.
Peter Ebenfelt, Ming Xiao, Hang Xu
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Analytic Group Kernels and Lie Algebra Kernels [PDF]
Transactions of the American Mathematical Society, 1960 Made available in DSpace on 2014-12-05T21:50:04Z (GMT). No. of bitstreams: 1 0013519.pdf: 4191483 bytes, checksum: 8be73568683b59703b526632ade49bfc (MD5) Previous issue date: 1955 ; Embargo set by: Seth Robbins for item 58849 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs ; Restricted to ...
R. A. Macauley
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Data‐driven performance metrics for neural network learning
International Journal of Adaptive Control and Signal Processing, EarlyView., 2023 Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri +2 more
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Multiresolution kernel matrix algebra [PDF]
Numerische MathematikAbstractWe propose a sparse algebra for samplet compressed kernel matrices to enable efficient scattered data analysis. We show that the compression of kernel matrices by means of samplets produces optimally sparse matrices in a certain S-format. The compression can be performed in cost and memory that scale essentially linearly with the number of data
H. Harbrecht +3 more
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On the extension of positive definite kernels to topological algebras [PDF]
Journal of Mathematical Physics, 2020 We define an extension of operator-valued positive definite functions from the real or complex setting to topological algebras and describe their associated reproducing kernel spaces. The case of entire functions is of special interest, and we give a precise meaning to some power series expansions of analytic functions that appears in many algebras.
Daniel Alpay, Ismael L. Paiva
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Kernel algebras and generalized Fourier-Mukai transforms
Journal of Noncommutative Geometry, 2008 We introduce and study kernel algebras , i.e., algebras in the category of sheaves on a square of a scheme, where the latter category is equipped with a monoidal structure via a natural convolution operation. We show that many interesting categories, such as D-modules, equivariant sheaves and their twisted versions,
Alexander Polishchuk
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Kernel Approximation on Algebraic Varieties
SIAM Journal on Applied Algebra and Geometry, 2023 Low-rank approximation of kernels is a fundamental mathematical problem with widespread algorithmic applications. Often the kernel is restricted to an algebraic variety, e.g., in problems involving sparse or low-rank data. We show that significantly better approximations are obtainable in this setting: the rank required to achieve a given error depends
Jason M. Altschuler, Pablo A. Parrilo
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