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  2. Matrix (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Matrix_(mathematics)

    where I n is the identity matrix of size n. An orthogonal matrix A is necessarily invertible (with inverse A −1 = A T), unitary (A −1 = A*), and normal (A*A = AA*). The determinant of any orthogonal matrix is either +1 or −1. A special orthogonal matrix is an orthogonal matrix with determinant +1.

  3. MATLAB - Wikipedia

    en.wikipedia.org/wiki/MATLAB

    MATLAB (an abbreviation of "MATrix LABoratory" [22]) is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms , creation of user interfaces , and interfacing with programs written in other languages.

  4. Vectorization (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Vectorization_(mathematics)

    The matrix vectorization operation can be written in terms of a linear sum. ... B i consists of n block matrices of size m × m, ... In Matlab/GNU Octave a matrix A ...

  5. Generalized minimal residual method - Wikipedia

    en.wikipedia.org/wiki/Generalized_minimal...

    After m iterations, where m is the size of the matrix A, the Krylov space K m is the whole of R m and hence the GMRES method arrives at the exact solution. However, the idea is that after a small number of iterations (relative to m ), the vector x n is already a good approximation to the exact solution.

  6. Identity matrix - Wikipedia

    en.wikipedia.org/wiki/Identity_matrix

    In linear algebra, the identity matrix of size is the square matrix with ones on the main diagonal and zeros elsewhere. It has unique properties, for example when the identity matrix represents a geometric transformation, the object remains unchanged by the transformation. In other contexts, it is analogous to multiplying by the number 1.

  7. 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:

  8. Matrix norm - Wikipedia

    en.wikipedia.org/wiki/Matrix_norm

    Suppose a vector norm ‖ ‖ on and a vector norm ‖ ‖ on are given. Any matrix A induces a linear operator from to with respect to the standard basis, and one defines the corresponding induced norm or operator norm or subordinate norm on the space of all matrices as follows: ‖ ‖, = {‖ ‖: ‖ ‖ =} = {‖ ‖ ‖ ‖:} . where denotes the supremum.

  9. Matrix multiplication - Wikipedia

    en.wikipedia.org/wiki/Matrix_multiplication

    Hadamard product of two matrices of the same size, resulting in a matrix of the same size, which is the product entry-by-entry; Kronecker product or tensor product, the generalization to any size of the preceding; Khatri-Rao product and Face-splitting product