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Titlebook: Hilbert Space, Boundary Value Problems and Orthogonal Polynomials; Allan M. Krall Book 2002 Springer Basel AG 2002 Boundary value problem.

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發(fā)表于 2025-3-21 18:57:20 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Hilbert Space, Boundary Value Problems and Orthogonal Polynomials
編輯Allan M. Krall
視頻videohttp://file.papertrans.cn/428/427075/427075.mp4
叢書(shū)名稱Operator Theory: Advances and Applications
圖書(shū)封面Titlebook: Hilbert Space, Boundary Value Problems and Orthogonal Polynomials; Allan M. Krall Book 2002 Springer Basel AG 2002 Boundary value problem.
描述The following tract is divided into three parts: Hilbert spaces and their (bounded and unbounded) self-adjoint operators, linear Hamiltonian systemsand their scalar counterparts and their application to orthogonal polynomials. In a sense, this is an updating of E. C. Titchmarsh‘s classic Eigenfunction Expansions. My interest in these areas began in 1960-61, when, as a graduate student, I was introduced by my advisors E. J. McShane and Marvin Rosenblum to the ideas of Hilbert space. The next year I was given a problem by Marvin Rosenblum that involved a differential operator with an "integral" boundary condition. That same year I attended a class given by the Physics Department in which the lecturer discussed the theory of Schwarz distributions and Titchmarsh‘s theory of singular Sturm-Liouville boundary value problems. I think a Professor Smith was the in- structor, but memory fails. Nonetheless, I am deeply indebted to him, because, as we shall see, these topics are fundamental to what follows. I am also deeply indebted to others. First F. V. Atkinson stands as a giant in the field. W. N. Everitt does likewise. These two were very encouraging to me during my younger (and later) ye
出版日期Book 2002
關(guān)鍵詞Boundary value problem; Differential equations; Hilbert space; differential equation; orthogonal polynom
版次1
doihttps://doi.org/10.1007/978-3-0348-8155-5
isbn_softcover978-3-0348-9459-3
isbn_ebook978-3-0348-8155-5Series ISSN 0255-0156 Series E-ISSN 2296-4878
issn_series 0255-0156
copyrightSpringer Basel AG 2002
The information of publication is updating

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發(fā)表于 2025-3-21 23:58:52 | 只看該作者
Allan M. Krall methods in recent years. Though dynamic information plays important role in facial expression recognition, utilizing the entire dynamic information of expression image sequences have high computational cost compared to the static cases. In order to reduce the computational cost, only neutral and em
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發(fā)表于 2025-3-22 00:41:51 | 只看該作者
Allan M. Krall methods in recent years. Though dynamic information plays important role in facial expression recognition, utilizing the entire dynamic information of expression image sequences have high computational cost compared to the static cases. In order to reduce the computational cost, only neutral and em
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發(fā)表于 2025-3-22 06:08:11 | 只看該作者
Allan M. Krall methods in recent years. Though dynamic information plays important role in facial expression recognition, utilizing the entire dynamic information of expression image sequences have high computational cost compared to the static cases. In order to reduce the computational cost, only neutral and em
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發(fā)表于 2025-3-22 08:51:23 | 只看該作者
Allan M. Krall methods in recent years. Though dynamic information plays important role in facial expression recognition, utilizing the entire dynamic information of expression image sequences have high computational cost compared to the static cases. In order to reduce the computational cost, only neutral and em
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發(fā)表于 2025-3-23 00:47:33 | 只看該作者
Allan M. Krallhe emergence of population-feeding issues, and increased use of pesticides, fertilisers, uneven irrigation scheduling, the appearance of pests and diseases, etc., are leading to a sharp decline in overall production as well as disturbed soil microbial communities and soil quality maintenance. Smart
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發(fā)表于 2025-3-23 09:10:02 | 只看該作者
Allan M. Krallnsformed the way agriculture is approached through precision agriculture. This approach leverages AI algorithms and data analytics to optimize farming practices at a granular level. By utilizing technologies like remote sensing, drones, and satellite imagery, farmers can collect extensive data on cr
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