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Encyclopedia > Matrix decomposition

In the mathematical discipline of linear algebra, a matrix decomposition is a factorization of a matrix into some canonical form. There are several different decompositions of a given matrix and the decomposition used depends on the problem we want to solve. In numerical analysis for example different decompositions are used to implement efficient matrix algorithms.


Example

When solving a system of linear equations the matrix A can be decomposed via the LU decomposition. The LU decomposition factorizes a matrix into a lower triangular matrix L and an upper triangular matrix U. The matrices L and U are much easier to solve than the original matrix A.


See also

Topics in mathematics related to linear algebra

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Vectors | Vector spaces | Linear span | Linear transformation | Linear independence | Linear combination | Basis | Column space | Row space | Dual space | Orthogonality | Eigenvector | Eigenvalue | Least squares regressions | Outer product | Cross product | Dot product | Transpose | Matrix decomposition


  Results from FactBites:
 
NationMaster - Encyclopedia: Matrix decomposition (348 words)
The rest of the matrix is an n×n orthogonal matrix; thus O(n) is a subgroup of O(n+1) (and of all higher groups).
The polar decomposition factors a matrix into a pair, one of which is the unique closest orthogonal matrix to the given matrix, or one of the closest if the given matrix is singular.
A matrix of dimension m by 1 is called a column vector; a matrix of dimension 1 by n is called a row vector.
Egwald Mathematics - Linear Algebra: Matrices and Matrix Decomposition (3984 words)
Thus, an identity matrix is a diagonal matrix with 1's along its diagonal and zeros elsewhere.
Matrix addition is commutative, since A + B = B + A. Subtraction of Matrices.
The actual L matrix is obtained from the RHS matrix of the final tableau by placing ones along its diagonal.
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