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Titlebook: Computer Algebra in Scientific Computing; 24th International W Fran?ois Boulier,Matthew England,Evgenii V. Vorozh Conference proceedings 20

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發(fā)表于 2025-3-23 11:31:28 | 只看該作者
12#
發(fā)表于 2025-3-23 15:21:03 | 只看該作者
https://doi.org/10.1007/978-3-319-64218-5cessing of discrete data, such as strings or graphs. Concerning continuous data, already Alan Turing had applied “his” machines to formalize and study the processing of real numbers: an aspect of his oeuvre that we transform from theory to practice..The present essay surveys the state of the art and
13#
發(fā)表于 2025-3-23 20:11:13 | 只看該作者
Giuseppe Lami,Fabrizio Fabbrini,Mario Fusaniordinary representation theory. We provide an algorithm to compute the equivariant Hilbert series of automorphisms acting on canonical rings of projective curves, using the formulas of Chevalley and Weil. Further, we apply our results on Fermat curves, determine explicitly the respective equivariant
14#
發(fā)表于 2025-3-23 23:21:22 | 只看該作者
15#
發(fā)表于 2025-3-24 06:19:38 | 只看該作者
16#
發(fā)表于 2025-3-24 09:17:51 | 只看該作者
17#
發(fā)表于 2025-3-24 12:04:03 | 只看該作者
https://doi.org/10.1007/978-3-030-85521-5f two- and one-dimensional invariant manifolds are presented, and the manifolds themselves are found with the use of computer algebra tools. From a mechanical point of view, these solutions correspond to equilibrium positions of the system. Their instability in the first approximation is proved.
18#
發(fā)表于 2025-3-24 18:20:32 | 只看該作者
19#
發(fā)表于 2025-3-24 19:35:52 | 只看該作者
Murat Yilmaz,Paul Clarke,Michael Reineridered. Two approaches are discussed: the one is reducing the problem to a constrained optimization problem in . with a quartic objective function, and the other one is connected with the singular value analysis for an appropriate matrix in .. Several examples are presented including classes of matr
20#
發(fā)表于 2025-3-25 00:15:40 | 只看該作者
Jan Pries-Heje,J?rn Johansen,Morten Korsaations is rare in computer algebra. Therefore, recently, computer algebra models have been combined with Gaussian processes, a regression model in machine learning, to describe the behavior of certain differential equations under data. While it was possible to describe polynomial boundary conditions
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