About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 102. Chapters: Determinant, Trace, Spectral theorem, Matrix multiplication, Singular value decomposition, Perron-Frobenius theorem, Jordan normal form, Moore-Penrose pseudoinverse, Matrix exponential, Non-negative matrix factorization, Invertible matrix, Eigendecomposition of a matrix, Kronecker product, Proofs involving the Moore-Penrose pseudoinverse, Matrix calculus, Jordan matrix, Square root of a matrix, Smith normal form, Logarithm of a matrix, Computing the permanent, Logarithmic norm, Quasideterminant, Matrix decomposition, Polar decomposition, Change of basis, Block matrix pseudoinverse, Eigenvalues and eigenvectors of the second derivative, Adjugate matrix, Gershgorin circle theorem, Trace diagram, Minor, Sylvester's criterion, Minimal polynomial, Schur decomposition, Sherman-Morrison formula, Specht's theorem, Kronecker sum of discrete Laplacians, Laplace expansion, Sylvester's law of inertia, Matrix determinant lemma, Numerical range, Unipotent, Matrix function, Matrix ring, Binomial inverse theorem, Jacobi's formula, Freivald's algorithm, Matrix unit, Minimum degree algorithm, Orthogonal Procrustes problem, Lie product formula, Frobenius covariant, Sylvester's formula, Nullity theorem, Coppersmith-Winograd algorithm, Mutual coherence, Cracovian, Field of values, P-matrix, Cuthill-McKee algorithm, Immanant of a matrix, Commuting matrices, Golden-Thompson inequality, Weighing matrix, Schur-Horn theorem, Spark, Spread of a matrix, Submatrix, Bidiagonalization, Sylvester's determinant theorem, Sparse graph code, Von Neumann's trace inequality, Spectral abscissa, Sinkhorn's theorem, Spectrum of a matrix, Totally positive matrix. Excerpt: In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix, with many useful applications in signal processing and statistics. Formally, the ...
