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  2. Rank–nullity theorem - Wikipedia

    en.wikipedia.org/wiki/Rank–nullity_theorem

    Rank–nullity theorem. The rank–nullity theorem is a theorem in linear algebra, which asserts: the number of columns of a matrix M is the sum of the rank of M and the nullity of M; and; the dimension of the domain of a linear transformation f is the sum of the rank of f (the dimension of the image of f) and the nullity of f (the dimension of ...

  3. Nullity (graph theory) - Wikipedia

    en.wikipedia.org/wiki/Nullity_(graph_theory)

    The nullity of M is given by m − n + c, where, c is the number of components of the graph and n − c is the rank of the oriented incidence matrix. This name is rarely used; the number is more commonly known as the cycle rank, cyclomatic number, or circuit rank of the graph. It is equal to the rank of the cographic matroid of the graph.

  4. Row and column spaces - Wikipedia

    en.wikipedia.org/wiki/Row_and_column_spaces

    The nullity of a matrix is the dimension of the null space, and is equal to the number of columns in the reduced row echelon form that do not have pivots. [7] The rank and nullity of a matrix A with n columns are related by the equation:

  5. Nullity theorem - Wikipedia

    en.wikipedia.org/wiki/Nullity_theorem

    More generally, if a submatrix is formed from the rows with indices {i 1, i 2, …, i m} and the columns with indices {j 1, j 2, …, j n}, then the complementary submatrix is formed from the rows with indices {1, 2, …, N} \ {j 1, j 2, …, j n} and the columns with indices {1, 2, …, N} \ {i 1, i 2, …, i m}, where N is the size of the ...

  6. Rank (linear algebra) - Wikipedia

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

    Once in row echelon form, the rank is clearly the same for both row rank and column rank, and equals the number of pivots (or basic columns) and also the number of non-zero rows. For example, the matrix A given by = [] can be put in reduced row-echelon form by using the following elementary row operations: [] + [] + [] + [] + [] . The final ...

  7. Kernel (linear algebra) - Wikipedia

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

    The left null space of A is the same as the kernel of A T. The left null space of A is the orthogonal complement to the column space of A, and is dual to the cokernel of the associated linear transformation. The kernel, the row space, the column space, and the left null space of A are the four fundamental subspaces associated with the matrix A.

  8. Sparse matrix - Wikipedia

    en.wikipedia.org/wiki/Sparse_matrix

    The array ROW_INDEX is of length m + 1 and encodes the index in V and COL_INDEX where the given row starts. This is equivalent to ROW_INDEX[j] encoding the total number of nonzeros above row j. The last element is NNZ, i.e., the fictitious index in V immediately after the last valid index NNZ − 1. [8]

  9. Sylvester's law of inertia - Wikipedia

    en.wikipedia.org/wiki/Sylvester's_law_of_inertia

    Sylvester's law of inertia states that the number of coefficients of a given sign is an invariant of ⁠ ⁠, i.e., does not depend on a particular choice of diagonalizing basis. Expressed geometrically, the law of inertia says that all maximal subspaces on which the restriction of the quadratic form is positive definite (respectively, negative ...