Linear Algebra for Everyone (美國原版)

Gilbert Strang

  • 出版商: Cambridge
  • 出版日期: 2020-12-31
  • 售價: $2,700
  • 貴賓價: 9.5$2,565
  • 語言: 英文
  • 頁數: 368
  • ISBN: 1733146636
  • ISBN-13: 9781733146630
  • 相關分類: 線性代數 Linear-algebra
  • 立即出貨



Linear algebra has become the subject to know for people in quantitative disciplines of all kinds. No longer the exclusive domain of mathematicians and engineers, it is now used everywhere there is data and everybody who works with data needs to know more. This new book from Professor Gilbert Strang, author of the acclaimed Introduction to Linear Algebra, now in its fifth edition, makes linear algebra accessible to everybody, not just those with a strong background in mathematics. It takes a more active start, beginning by finding independent columns of small matrices, leading to the key concepts of linear combinations and rank and column space. From there it passes on to the classical topics of solving linear equations, orthogonality, linear transformations and subspaces, all clearly explained with many examples and exercises. The last major topics are eigenvalues and the important singular value decomposition, illustrated with applications to differential equations and image compression. A final optional chapter explores the ideas behind deep learning.

- Author is a world-renowned teacher of linear algebra who delivers the material in a clear and effective way that students will appreciate
- Uses a highly accessible approach that enables students without a strong mathematics background to understand more advanced topics such as singular value decomposition (SVD)
- Covers topics such as data science and deep learning that show why linear algebra isn't just for mathematicians
- Comes with accompanying video lectures on the MIT OpenCourseWare website, giving students the option to self-study and learn at their own pace


1. Vectors and Matrices
2. Solving Linear Equations Ax = b
3. The Four Fundamental Subspaces
4. Orthogonality
5. Determinants and Linear Transformations
6. Eigenvalues and Eigenvectors
7. The Singular Value Decomposition (SVD)
8. Learning from Data
Appendix 1. The Ranks of AB and A + B
Appendix 2. Eigenvalues and Singular Values: Rank One
Appendix 3. Counting Parameters in the Basic Factorizations
Appendix 4. Codes and Algorithms for Numerical Linear Algebra
Appendix 5. Matrix Factorizations
Appendix 6. The Column-Row Factorization of a Matrix
Appendix 7. The Jordan Form of a Square Matrix
Appendix 8. Tensors
Appendix 9. The Condition Number
Appendix 10. Markov Matrices and Perron-Frobenius
Index of Symbols
Six Great Theorems / Linear Algebra in a Nutshell.