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A complex symmetric matrix can be 'diagonalized' using a unitary matrix: ... is a product of a lower-triangular matrix and its transpose, =. If the matrix is ...
If A is an m × n matrix and A T is its transpose, then the result of matrix multiplication with these two matrices gives two square matrices: A A T is m × m and A T A is n × n. Furthermore, these products are symmetric matrices. Indeed, the matrix product A A T has entries that are the inner product of a row of A with a column of A T.
Complex symmetric matrix – Matrix equal to its transpose Haynsworth inertia additivity formula – Counts positive, negative, and zero eigenvalues of a block partitioned Hermitian matrix Hermitian form – Generalization of a bilinear form Pages displaying short descriptions of redirect targets
In mathematics, particularly in linear algebra, a skew-symmetric (or antisymmetric or antimetric [1]) matrix is a square matrix whose transpose equals its negative. That is, it satisfies the condition [ 2 ] : p. 38
In mathematics, a symmetric matrix with real entries is positive-definite if the real number is positive for every nonzero real column vector, where is the row vector transpose of . [1] More generally, a Hermitian matrix (that is, a complex matrix equal to its conjugate transpose) is positive-definite if the real number is positive for every nonzero complex column vector , where denotes the ...
The conjugate transpose of a matrix with real entries reduces to the transpose of , as the conjugate of a real number is the number itself. The conjugate transpose can be motivated by noting that complex numbers can be usefully represented by 2 × 2 {\displaystyle 2\times 2} real matrices, obeying matrix addition and multiplication: [ 3 ]
In linear algebra, the Cholesky decomposition or Cholesky factorization (pronounced / ʃ ə ˈ l ɛ s k i / shə-LES-kee) is a decomposition of a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose, which is useful for efficient numerical solutions, e.g., Monte Carlo simulations.
Sparse matrix algorithms can tackle huge sparse matrices that are utterly impractical for dense matrix algorithms. Symmetric matrix: A square matrix which is equal to its transpose, A = A T (a i,j = a j,i). Toeplitz matrix: A matrix with constant diagonals. Totally positive matrix: A matrix with determinants of all its square submatrices ...