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  2. Sparse matrix - Wikipedia

    en.wikipedia.org/wiki/Sparse_matrix

    In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix in which most of the elements are zero. [1] There is no strict definition regarding the proportion of zero-value elements for a matrix to qualify as sparse but a common criterion is that the number of non-zero elements is roughly equal to the number of ...

  3. Category:Sparse matrices - Wikipedia

    en.wikipedia.org/wiki/Category:Sparse_matrices

    Pages in category "Sparse matrices" The following 19 pages are in this category, out of 19 total. ... Sparse matrix–vector multiplication; T. Tridiagonal matrix; Z.

  4. Minimum degree algorithm - Wikipedia

    en.wikipedia.org/wiki/Minimum_degree_algorithm

    In numerical analysis, the minimum degree algorithm is an algorithm used to permute the rows and columns of a symmetric sparse matrix before applying the Cholesky decomposition, to reduce the number of non-zeros in the Cholesky factor. This results in reduced storage requirements and means that the Cholesky factor can be applied with fewer ...

  5. Band matrix - Wikipedia

    en.wikipedia.org/wiki/Band_matrix

    As sparse matrices lend themselves to more efficient computation than dense matrices, as well as in more efficient utilization of computer storage, there has been much research focused on finding ways to minimise the bandwidth (or directly minimise the fill-in) by applying permutations to the matrix, or other such equivalence or similarity ...

  6. LU decomposition - Wikipedia

    en.wikipedia.org/wiki/LU_decomposition

    For example, for a 3 × 3 matrix A, ... Sparse-matrix decomposition. Special algorithms have been developed for factorizing large sparse matrices.

  7. List of named matrices - Wikipedia

    en.wikipedia.org/wiki/List_of_named_matrices

    A rearrangement of the entries of a banded matrix which requires less space. Sparse matrix: A matrix with relatively few non-zero elements. Sparse matrix algorithms can tackle huge sparse matrices that are utterly impractical for dense matrix algorithms. Symmetric matrix: A square matrix which is equal to its transpose, A = A T (a i,j = a j,i ...

  8. Adjacency matrix - Wikipedia

    en.wikipedia.org/wiki/Adjacency_matrix

    They can, for example, be used to represent sparse graphs without incurring the space overhead from storing the many zero entries in the adjacency matrix of the sparse graph. In the following section the adjacency matrix is assumed to be represented by an array data structure so that zero and non-zero entries are all directly represented in ...

  9. Matrix representation - Wikipedia

    en.wikipedia.org/wiki/Matrix_representation

    Matrix representation is a method used by a computer language to store column-vector matrices of more than one dimension in memory. Fortran and C use different schemes for their native arrays. Fortran uses "Column Major" ( AoS ), in which all the elements for a given column are stored contiguously in memory.