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  2. 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.

  3. 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 ...

  4. Matroid rank - Wikipedia

    en.wikipedia.org/wiki/Matroid_rank

    In graph theory, the circuit rank (or cyclomatic number) of a graph is the corank of the associated graphic matroid; it measures the minimum number of edges that must be removed from the graph to make the remaining edges form a forest. [5] Several authors have studied the parameterized complexity of graph algorithms parameterized by this number ...

  5. 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 ...

  6. 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 ...

  7. Markov chain - Wikipedia

    en.wikipedia.org/wiki/Markov_chain

    For i ≠ j, the elements q ij are non-negative and describe the rate of the process transitions from state i to state j. The elements q ii are chosen such that each row of the transition rate matrix sums to zero, while the row-sums of a probability transition matrix in a (discrete) Markov chain are all equal to one.

  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. List of named matrices - Wikipedia

    en.wikipedia.org/wiki/List_of_named_matrices

    A matrix whose elements are of the form 1/(x i + y j) for (x i), (y j) injective sequences (i.e., taking every value only once). Centrosymmetric matrix: A matrix symmetric about its center; i.e., a ij = a n−i+1,n−j+1. Circulant matrix: A matrix where each row is a circular shift of its predecessor. Conference matrix