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"Matrix algebra" as a book category covers the theory, methods, and applications of matrices and linear transformations: basic operations (addition, multiplication, inverses), determinants, rank, systems of linear equations, eigenvalues/eigenvectors and diagonalization, canonical forms, and matrix factorizations (LU, QR, SVD). It spans pure mathematical foundations (vector spaces, linear mappings, quadratic forms), computational and numerical techniques for large or sparse systems, and applied topics in engineering, physics, computer science and data science (signal processing, control theory, optimization, machine learning). Titles may range from introductory textbooks for undergraduates to advanced theoretical treatments and practical guides to algorithms and software implementations, often emphasizing both conceptual understanding and computational tools.

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