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Matrix multiplication was first described by the French mathematician Jacques Philippe Marie Binet in 1812, [2] to represent the composition of linear maps that are represented by matrices. Matrix multiplication is thus a basic tool of linear algebra , and as such has numerous applications in many areas of mathematics, as well as in applied ...
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:
Note first that any 2 × 2 real matrix can be considered one of the three types of the complex number z = x + y ε, where ε 2 ∈ { −1, 0, +1 }. This z is a point on a complex subplane of the ring of matrices. [8] The case where the determinant is negative only arises in a plane with ε 2 =+1, that is a split-complex number plane. Only one ...
Matrices can be used to compactly write and work with multiple linear equations, that is, systems of linear equations. For example, if A is an m×n matrix, x designates a column vector (that is, n×1-matrix) of n variables x 1, x 2, ..., x n, and b is an m×1-column vector, then the matrix equation =
An Toeplitz matrix may be defined as a matrix where , =, for constants , …,.The set of Toeplitz matrices is a subspace of the vector space of matrices (under matrix addition and scalar multiplication).
In linear algebra, the Cholesky decomposition or Cholesky factorization (pronounced / ʃ ə ˈ l ɛ s k i / shə-LES-kee) is a decomposition of a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose, which is useful for efficient numerical solutions, e.g., Monte Carlo simulations.