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  2. Diagonally dominant matrix - Wikipedia

    en.wikipedia.org/wiki/Diagonally_dominant_matrix

    A strictly diagonally dominant matrix (or an irreducibly diagonally dominant matrix [2]) is non-singular. A Hermitian diagonally dominant matrix with real non-negative diagonal entries is positive semidefinite. This follows from the eigenvalues being real, and Gershgorin's circle theorem. If the symmetry requirement is eliminated, such a matrix ...

  3. Weakly chained diagonally dominant matrix - Wikipedia

    en.wikipedia.org/wiki/Weakly_chained_diagonally...

    Weakly diagonally dominant (WDD) is defined with instead. The directed graph associated with an m × m {\displaystyle m\times m} complex matrix A = ( a i j ) {\displaystyle A=(a_{ij})} is given by the vertices { 1 , … , m } {\displaystyle \{1,\ldots ,m\}} and edges defined as follows: there exists an edge from i → j {\displaystyle i ...

  4. Jacobi method - Wikipedia

    en.wikipedia.org/wiki/Jacobi_method

    A sufficient (but not necessary) condition for the method to converge is that the matrix A is strictly or irreducibly diagonally dominant. Strict row diagonal dominance means that for each row, the absolute value of the diagonal term is greater than the sum of absolute values of other terms:

  5. Row- and column-major order - Wikipedia

    en.wikipedia.org/wiki/Row-_and_column-major_order

    Even though the row is indicated by the first index and the column by the second index, no grouping order between the dimensions is implied by this. The choice of how to group and order the indices, either by row-major or column-major methods, is thus a matter of convention. The same terminology can be applied to even higher dimensional arrays.

  6. Gaussian elimination - Wikipedia

    en.wikipedia.org/wiki/Gaussian_elimination

    Animation of Gaussian elimination. Red row eliminates the following rows, green rows change their order. In mathematics, Gaussian elimination, also known as row reduction, is an algorithm for solving systems of linear equations. It consists of a sequence of row-wise operations performed on the corresponding matrix of coefficients.

  7. Invariant subspace - Wikipedia

    en.wikipedia.org/wiki/Invariant_subspace

    Then Lat(End(V))={0,V}. Given a representation of a group G on a vector space V , we have a linear transformation T ( g ) : V → V for every element g of G . If a subspace W of V is invariant with respect to all these transformations, then it is a subrepresentation and the group G acts on W in a natural way.

  8. Weight (representation theory) - Wikipedia

    en.wikipedia.org/wiki/Weight_(representation_theory)

    In the case, the dominant integral elements live in a 60-degree sector. The notion of being dominant is not the same as being higher than zero. Note the grey area in the picture on the right is a 120-degree sector, strictly containing the 60-degree sector corresponding to the dominant integral elements.

  9. Anti-diagonal matrix - Wikipedia

    en.wikipedia.org/wiki/Anti-diagonal_matrix

    In mathematics, an anti-diagonal matrix is a square matrix where all the entries are zero except those on the diagonal going from the lower left corner to the upper right corner (↗), known as the anti-diagonal (sometimes Harrison diagonal, secondary diagonal, trailing diagonal, minor diagonal, off diagonal or bad diagonal).