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  2. Symmetric matrix - Wikipedia

    en.wikipedia.org/wiki/Symmetric_matrix

    A complex symmetric matrix can be 'diagonalized' using a unitary matrix: thus if is a complex symmetric matrix, there is a unitary matrix such that is a real diagonal matrix with non-negative entries.

  3. Rank (linear algebra) - Wikipedia

    en.wikipedia.org/wiki/Rank_(linear_algebra)

    A matrix that has rank min(m, n) is said to have full rank; otherwise, the matrix is rank deficient. Only a zero matrix has rank zero. f is injective (or "one-to-one") if and only if A has rank n (in this case, we say that A has full column rank). f is surjective (or "onto") if and only if A has rank m (in this case, we say that A has full row ...

  4. Invertible matrix - Wikipedia

    en.wikipedia.org/wiki/Invertible_matrix

    An example with rank of n ... The general 3 × 3 inverse can be expressed concisely in terms of ... If the given invertible matrix is a symmetric matrix with ...

  5. Rank factorization - Wikipedia

    en.wikipedia.org/wiki/Rank_factorization

    Every finite-dimensional matrix has a rank decomposition: Let be an matrix whose column rank is . Therefore, there are r {\textstyle r} linearly independent columns in A {\textstyle A} ; equivalently, the dimension of the column space of A {\textstyle A} is r {\textstyle r} .

  6. List of named matrices - Wikipedia

    en.wikipedia.org/wiki/List_of_named_matrices

    A matrix with constant skew-diagonals; also an upside down Toeplitz matrix. A square Hankel matrix is symmetric. Hermitian matrix: A square matrix which is equal to its conjugate transpose, A = A *. Hessenberg matrix: An "almost" triangular matrix, for example, an upper Hessenberg matrix has zero entries below the first subdiagonal. Hollow matrix

  7. Symmetric tensor - Wikipedia

    en.wikipedia.org/wiki/Symmetric_tensor

    This minimal decomposition is called a Waring decomposition; it is a symmetric form of the tensor rank decomposition. For second-order tensors this corresponds to the rank of the matrix representing the tensor in any basis, and it is well known that the maximum rank is equal to the dimension of the underlying vector space.

  8. Laplacian matrix - Wikipedia

    en.wikipedia.org/wiki/Laplacian_matrix

    The Laplacian matrix of a directed graph is by definition generally non-symmetric, while, e.g., traditional spectral clustering is primarily developed for undirected graphs with symmetric adjacency and Laplacian matrices. A trivial approach to applying techniques requiring the symmetry is to turn the original directed graph into an undirected ...

  9. Idempotent matrix - Wikipedia

    en.wikipedia.org/wiki/Idempotent_matrix

    This provides an easy way of computing the rank, or alternatively an easy way of determining the trace of a matrix whose elements are not specifically known (which is helpful in statistics, for example, in establishing the degree of bias in using a sample variance as an estimate of a population variance).