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Total Size:
12.6 MB
Info Hash:
8400BC886675B49337D35D3C00867591A1E27E37
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Added:
July 13, 2025, 11:38 a.m.
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(Last updated: July 13, 2025, 11:39 a.m.)
| File | Size |
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| ['Readme.txt'] | 0 bytes |
| ['Axler S. Linear Algebra Done Right 3ed 2015.pdf'] | 0 bytes |
| ['Axler S. Linear Algebra Done Right. Solutions Manual 4ed 2024.pdf'] | 0 bytes |
| ['Axler S. Linear Algebra Done Right 4ed 2023.pdf'] | 0 bytes |
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SOURCE: Sheldon A. Linear Algebra Done Right. Solutions Manual 4ed 2024
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MEDIAINFO
Textbook in PDF format Now available in Open Access, this best-selling textbook for a second course in linear algebra is aimed at undergraduate math majors and graduate students. The fourth edition gives an expanded treatment of the singular value decomposition and its consequences. It includes a new chapter on multilinear algebra, treating bilinear forms, quadratic forms, tensor products, and an approach to determinants via alternating multilinear forms. This new edition also increases the use of the minimal polynomial to provide cleaner proofs of multiple results. Also, over 250 new exercises have been added. The novel approach taken here banishes determinants to the end of the book. The text focuses on the central goal of linear algebra: understanding the structure of linear operators on finite-dimensional vector spaces. The author has taken unusual care to motivate concepts and simplify proofs. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. Beautiful formatting creates pages with an unusually student-friendly appearance in both print and electronic versions. No prerequisites are assumed other than the usual demand for suitable mathematical maturity. The text starts by discussing vector spaces, linear independence, span, basis, and dimension. The book then deals with linear maps, eigenvalues, and eigenvectors. Inner-product spaces are introduced, leading to the finite-dimensional spectral theorem and its consequences. Generalized eigenvectors are then used to provide insight into the structure of a linear operator. Front Matter Vector Spaces Finite-Dimensional Vector Spaces Linear Maps Polynomials Eigenvalues and Eigenvectors Inner Product Spaces Operators on Inner Product Spaces Operators on Complex Vector Spaces Multilinear Algebra and Determinants Back Matter
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