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Titlebook: Harmonic Analysis on Reductive Groups; William H. Barker,Paul J. Sally Book 1991 Springer Science+Business Media New York 1991 Fourier tra

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书目名称Harmonic Analysis on Reductive Groups
编辑William H. Barker,Paul J. Sally
视频videohttp://file.papertrans.cn/425/424281/424281.mp4
丛书名称Progress in Mathematics
图书封面Titlebook: Harmonic Analysis on Reductive Groups;  William H. Barker,Paul J. Sally Book 1991 Springer Science+Business Media New York 1991 Fourier tra
描述A conference on Harmonic Analysis on Reductive Groups was held at Bowdoin College in Brunswick, Maine from July 31 to August 11, 1989. The stated goal of the conference was to explore recent advances in harmonic analysis on both real and p-adic groups. It was the first conference since the AMS Summer Sym­ posium on Harmonic Analysis on Homogeneous Spaces, held at Williamstown, Massachusetts in 1972, to cover local harmonic analysis on reductive groups in such detail and to such an extent. While the Williamstown conference was longer (three weeks) and somewhat broader (nilpotent groups, solvable groups, as well as semisimple and reductive groups), the structure and timeliness of the two meetings was remarkably similar. The program of the Bowdoin Conference consisted of two parts. First, there were six major lecture series, each consisting of several talks addressing those topics in harmonic analysis on real and p-adic groups which were the focus of intensive research during the previous decade. These lectures began at an introductory level and advanced to the current state of research. Sec­ ond, there was a series of single lectures in which the speakers presented an overview of the
出版日期Book 1991
关键词Fourier transform; Irreducibility; calculus; harmonic analysis; lie group
版次1
doihttps://doi.org/10.1007/978-1-4612-0455-8
isbn_softcover978-1-4612-6768-3
isbn_ebook978-1-4612-0455-8Series ISSN 0743-1643 Series E-ISSN 2296-505X
issn_series 0743-1643
copyrightSpringer Science+Business Media New York 1991
The information of publication is updating

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Gerrit J. HeckmanIf industrial systems act autonomously, their choices and results must be within specified bounds to satisfy these requirements. Reinforcement learning (RL) is promising to find solutions that outperform known or handcrafted heuristics. However in industrial scenarios, it also is crucial to prevent
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