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  2. Diagonal matrix - Wikipedia

    en.wikipedia.org/wiki/Diagonal_matrix

    A matrix is diagonal if and only if it is both upper-and lower-triangular. A diagonal matrix is symmetric. The identity matrix I n and zero matrix are diagonal. A 1×1 matrix is always diagonal. The square of a 2×2 matrix with zero trace is always diagonal.

  3. Toeplitz matrix - Wikipedia

    en.wikipedia.org/wiki/Toeplitz_matrix

    The set of Toeplitz matrices is a subspace of the vector space of matrices (under matrix addition and scalar multiplication). Two Toeplitz matrices may be added in O ( n ) {\displaystyle O(n)} time (by storing only one value of each diagonal) and multiplied in O ( n 2 ) {\displaystyle O(n^{2})} time.

  4. Distance matrix - Wikipedia

    en.wikipedia.org/wiki/Distance_matrix

    In general, a distance matrix is a weighted adjacency matrix of some graph. In a network, a directed graph with weights assigned to the arcs, the distance between two nodes of the network can be defined as the minimum of the sums of the weights on the shortest paths joining the two nodes (where the number of steps in the path is bounded). [2]

  5. Hadamard product (matrices) - Wikipedia

    en.wikipedia.org/wiki/Hadamard_product_(matrices)

    The Hadamard product operates on identically shaped matrices and produces a third matrix of the same dimensions. In mathematics, the Hadamard product (also known as the element-wise product, entrywise product [1]: ch. 5 or Schur product [2]) is a binary operation that takes in two matrices of the same dimensions and returns a matrix of the multiplied corresponding elements.

  6. Matrix multiplication algorithm - Wikipedia

    en.wikipedia.org/wiki/Matrix_multiplication...

    The definition of matrix multiplication is that if C = AB for an n × m matrix A and an m × p matrix B, then C is an n × p matrix with entries = =. From this, a simple algorithm can be constructed which loops over the indices i from 1 through n and j from 1 through p, computing the above using a nested loop:

  7. Computational complexity of matrix multiplication - Wikipedia

    en.wikipedia.org/wiki/Computational_complexity...

    This means that, treating the input n×n matrices as block 2 × 2 matrices, the task of multiplying n×n matrices can be reduced to 7 subproblems of multiplying n/2×n/2 matrices. Applying this recursively gives an algorithm needing O ( n log 2 ⁡ 7 ) ≈ O ( n 2.807 ) {\displaystyle O(n^{\log _{2}7})\approx O(n^{2.807})} field operations.

  8. Idempotent matrix - Wikipedia

    en.wikipedia.org/wiki/Idempotent_matrix

    For idempotent diagonal matrices, and must be either 1 or 0. If b = c {\displaystyle b=c} , the matrix ( a b b 1 − a ) {\displaystyle {\begin{pmatrix}a&b\\b&1-a\end{pmatrix}}} will be idempotent provided a 2 + b 2 = a , {\displaystyle a^{2}+b^{2}=a,} so a satisfies the quadratic equation

  9. Min-plus matrix multiplication - Wikipedia

    en.wikipedia.org/wiki/Min-plus_matrix_multiplication

    Given two matrices = and = (), their distance product = = is defined as an matrix such that = = {+}. This is standard matrix multiplication for the semi-ring of tropical numbers in the min convention.