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Linear Algebra is a category of books focused on the theory and applications of vectors, matrices, and linear transformations. Titles typically cover systems of linear equations, matrix operations, determinants, rank, eigenvalues and eigenvectors, diagonalization, inner product spaces and orthogonality, and often extend to topics like least squares, singular value decomposition, and numerical methods. The category includes introductory textbooks, proof-oriented treatments, problem and solution manuals, and applied or computational guides that use tools such as MATLAB or Python/NumPy. Geared to audiences from advanced high school through graduate study and professionals, these books emphasize both conceptual understanding and practical techniques with applications across data science, machine learning, physics, engineering, computer graphics, economics, and optimization.

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