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The diagonals of a cube with side length 1. AC' (shown in blue) is a space diagonal with length , while AC (shown in red) is a face diagonal and has length .. In geometry, a diagonal is a line segment joining two vertices of a polygon or polyhedron, when those vertices are not on the same edge.
In linear algebra, a diagonal matrix is a matrix in which the entries outside the main diagonal are all zero; the term usually refers to square matrices. Elements of the main diagonal can either be zero or nonzero.
The trace of a Hermitian matrix is real, because the elements on the diagonal are real. The trace of a permutation matrix is the number of fixed points of the corresponding permutation, because the diagonal term a ii is 1 if the i th point is fixed and 0 otherwise. The trace of a projection matrix is the dimension of the target space.
In linear algebra, the main diagonal (sometimes principal diagonal, primary diagonal, leading diagonal, major diagonal, or good diagonal) of a matrix is the list of entries , where =. All off-diagonal elements are zero in a diagonal matrix. The following four matrices have their main diagonals indicated by red ones:
The definition in the first paragraph sums entries across each row. It is therefore sometimes called row diagonal dominance. If one changes the definition to sum down each column, this is called column diagonal dominance. Any strictly diagonally dominant matrix is trivially a weakly chained diagonally dominant matrix.
A can therefore be decomposed into a matrix composed of its eigenvectors, a diagonal matrix with its eigenvalues along the diagonal, and the inverse of the matrix of eigenvectors. This is called the eigendecomposition and it is a similarity transformation. Such a matrix A is said to be similar to the diagonal matrix Λ or diagonalizable.
Any matrix can be decomposed as = for some isometries , and diagonal nonnegative real matrix . The pseudoinverse can then be written as A + = V D + U ∗ {\displaystyle A^{+}=VD^{+}U^{*}} , where D + {\displaystyle D^{+}} is the pseudoinverse of D {\displaystyle D} and can be obtained by transposing the matrix and replacing the nonzero values ...
Closed form solutions can be computed for special cases such as symmetric matrices with all diagonal and off-diagonal elements equal [7] or Toeplitz matrices [8] and for the general case as well. [9] [10] In general, the inverse of a tridiagonal matrix is a semiseparable matrix and vice versa. [11]