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  2. LU decomposition - Wikipedia

    en.wikipedia.org/wiki/LU_decomposition

    An LU factorization with full pivoting involves both row and column permutations to find absolute maximum element in the whole submatrix: P A Q = L U , {\displaystyle PAQ=LU,} where L , U and P are defined as before, and Q is a permutation matrix that reorders the columns of A .

  3. Matrix decomposition - Wikipedia

    en.wikipedia.org/wiki/Matrix_decomposition

    When P is an identity matrix, the LUP decomposition reduces to the LU decomposition. Comments: The LUP and LU decompositions are useful in solving an n -by- n system of linear equations A x = b {\displaystyle A\mathbf {x} =\mathbf {b} } .

  4. Gaussian elimination - Wikipedia

    en.wikipedia.org/wiki/Gaussian_elimination

    Then the first part of the algorithm computes an LU decomposition, while the second part writes the original matrix as the product of a uniquely determined invertible matrix and a uniquely determined reduced row echelon matrix.

  5. Block LU decomposition - Wikipedia

    en.wikipedia.org/wiki/Block_LU_decomposition

    In linear algebra, a Block LU decomposition is a matrix decomposition of a block matrix into a lower block triangular matrix L and an upper block triangular matrix U. This decomposition is used in numerical analysis to reduce the complexity of the block matrix formula.

  6. Cholesky decomposition - Wikipedia

    en.wikipedia.org/wiki/Cholesky_decomposition

    The Cholesky decomposition is commonly used in the Monte Carlo method for simulating systems with multiple correlated variables. The covariance matrix is decomposed to give the lower-triangular L. Applying this to a vector of uncorrelated observations in a sample u produces a sample vector Lu with the covariance properties of the system being ...

  7. Eigendecomposition of a matrix - Wikipedia

    en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix

    The decomposition can be derived from the fundamental property of eigenvectors: = = =. The linearly independent eigenvectors q i with nonzero eigenvalues form a basis (not necessarily orthonormal) for all possible products Ax, for x ∈ C n, which is the same as the image (or range) of the corresponding matrix transformation, and also the ...

  8. Crout matrix decomposition - Wikipedia

    en.wikipedia.org/wiki/Crout_matrix_decomposition

    In linear algebra, the Crout matrix decomposition is an LU decomposition which decomposes a matrix into a lower triangular matrix (L), an upper triangular matrix (U) and, although not always needed, a permutation matrix (P). It was developed by Prescott Durand Crout. [1] The Crout matrix decomposition algorithm differs slightly from the ...

  9. Vandermonde matrix - Wikipedia

    en.wikipedia.org/wiki/Vandermonde_matrix

    In the process, it computes the LU decomposition of the Vandermonde matrix. The third proof is more elementary but more complicated, using only elementary row and column operations . First proof: Polynomial properties