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Titlebook: Linear Algebra; Serge Lang Textbook 1987Latest edition Springer Science+Business Media New York 1987 Eigenvalue.Eigenvector.algebra.linear

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發(fā)表于 2025-3-21 19:41:08 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Linear Algebra
編輯Serge Lang
視頻videohttp://file.papertrans.cn/587/586249/586249.mp4
叢書(shū)名稱Undergraduate Texts in Mathematics
圖書(shū)封面Titlebook: Linear Algebra;  Serge Lang Textbook 1987Latest edition Springer Science+Business Media New York 1987 Eigenvalue.Eigenvector.algebra.linear
描述Linear Algebra is intended for a one-term course at the junior or senior level. It begins with an exposition of the basic theory of vector spaces and proceeds to explain the fundamental structure theorems for linear maps, including eigenvectors and eigenvalues, quadric and hermitian forms, diagonalization of symmetric, hermitian, and unitary linear maps and matrices, triangulation, and Jordan canonical form. The book also includes a useful chapter on convex sets and the finite-dimensional Krein-Milman theorem. The presentation is aimed at the student who has already had some exposure to the elementary theory of matrices, determinants, and linear maps. However, the book is logically self-contained. In this new edition, many parts of the book have been rewritten and reorganized, and new exercises have been added.
出版日期Textbook 1987Latest edition
關(guān)鍵詞Eigenvalue; Eigenvector; algebra; linear algebra; matrices; matrix theory
版次3
doihttps://doi.org/10.1007/978-1-4757-1949-9
isbn_softcover978-1-4419-3081-1
isbn_ebook978-1-4757-1949-9Series ISSN 0172-6056 Series E-ISSN 2197-5604
issn_series 0172-6056
copyrightSpringer Science+Business Media New York 1987
The information of publication is updating

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沙發(fā)
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nced by this set of Nelson’s ideas, from a project of the author and collaborators critiquing and expanding the Kodu introductory programming environment. It also argues that we must unite work that follows in Nelson’s footsteps by . software critically (e.g., “software studies”) with work that aims
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Serge LangOversampling Technique (SMOTE). It averages two learning-based classifiers i.e. Multi-label Support Vector Machine and Multi-label Convolutional Neural Network based on offline handwriting recognition to produce one optimal predictive model. The model was trained using 1066 handwriting samples writt
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Serge Langrd – which means, crudely speaking, that in general, no computationally efficient algorithm can solve all particular cases of the corresponding problem. In this paper, we overview practical situations in which computationally efficient algorithms exist: e.g., situations when measurements are very ac
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